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Youssef, Azdoud, Amine Aouatif, Nassih Bouchra, and Ngadi Mohammed. "Self scale estimation of the tracking window merged with adaptive particle filter tracker." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 1 (February 1, 2023): 374. http://dx.doi.org/10.11591/ijece.v13i1.pp374-388.
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Tracking a mobile object is one of the important topics in pattern recognition, but style has some obstacles. A Reliable tracking system must adjust their tracking windows in real time according to appearance changes of the tracked object. Furthermore, it has to deal with many challenges when one or multiple objects need to be tracked, for instance when the target is partially or fully occluded, background clutter, or even some target region is blurred. In this paper, we will present a novel approach for a single object tracking that combines particle filter algorithm and kernel distribution that update its tracking window according to object scale changes, whose name is multi-scale adaptive particle filter tracker. We will demonstrate that the use of particle filter combined with kernel distribution inside the resampling process will provide more accurate object localization within a research area. Furthermore, its average error for target localization was significantly lower than 21.37 pixels as the mean value. We have conducted several experiments on real video sequences and compared acquired results to other existing state of the art trackers to demonstrate the effectiveness of the multi-scale adaptive particle filter tracker.
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Handegard, Nils Olav, and Kresimir Williams. "Automated tracking of fish in trawls using the DIDSON (Dual frequency IDentification SONar)." ICES Journal of Marine Science 65, no. 4 (March 11, 2008): 636–44. http://dx.doi.org/10.1093/icesjms/fsn029.
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Abstract Handegard, N. O., and Williams, K. 2008. Automated tracking of fish in trawls using the DIDSON (Dual frequency IDentification SONar). – ICES Journal of Marine Science. 65: 636–644. An application for the automated tracking of dual-frequency, identification sonar (DIDSON) data was developed and tested on fish observations taken in midwater trawls. The process incorporates target detection, multiple target tracking, and the extraction of behaviour information such as target speed and direction from the track data. The automatic tracker was evaluated using three test datasets with different target sizes, observation ranges, and densities. The targets in the datasets were tracked manually and with the automated tracker, using the manual-tracking results as the standard for estimating the performance of the automated tracking process. In the first and third dataset, where the targets were smaller and less dense, the automated tracking performed well, correctly identifying 74% and 57% of targets, respectively, and associating targets into tracks with <10% error compared with the manually tracked data. In the second dataset, where targets were dense and appeared large owing to the shorter observation range, 45% of targets were correctly identified, and the track error rate was 21%. Target speed and direction, derived from the tracking data, agreed well between the manual and automatic methods for all three test cases. Automated tracking represents a useful technique for processing DIDSON data, and a valuable alternative to time-consuming, manual data-processing, when used in appropriate conditions.
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Crebolder, Jacquelyn M., and Tarra L. Penney. "Use of Continuous Zoom on Electro-Optical Imaging Systems: Comparisons between Automatic and Manual Target Tracking." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 51, no. 19 (October 2007): 1301–5. http://dx.doi.org/10.1177/154193120705101904.
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The use of continuous zoom in an electro-optical sensor system was investigated with respect to target tracking. Using a simulation of an operator-machine interface in an airborne multi-sensor surveillance system, targets were tracked by manually directing the sensor or by an automated tracker. It was hypothesized that frequency of using the continuous zoom would be higher in the manual tracking mode than in auto-tracking, and negatively correlated with tracking error. Sensor, and targets to be tracked, were either moving or stationary in three types of tracking scenarios. Results showed that the zoom function was used more often when tracking manually, although the way continuous zoom was used differed between the two tracking modes. Also, tracking error was lower when the zoom function was used in manual mode. Tracking error was additionally affected by whether or not the target and/or the sensor were moving or stationary. Results improve our understanding of the way complex sensor systems are used, and will assist in ascertaining whether providing a continuous zoom into optical imaging systems is of benefit to operators.
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Shukla, P. K., S. Goel, P. Singh, and B. Lohani. "Automatic geolocation of targets tracked by aerial imaging platforms using satellite imagery." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1 (November 7, 2014): 381–88. http://dx.doi.org/10.5194/isprsarchives-xl-1-381-2014.
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Tracking of targets from aerial platforms is an important activity in several applications, especially surveillance. Knowled ge of geolocation of these targets adds additional significant and useful information to the application. This paper determines the geolocation of a target being tracked from an aerial platform using the technique of image registration. Current approaches utilize a POS to determine the location of the aerial platform and then use the same for geolocation of the targets using the principle of photogrammetry. The constraints of cost and low-payload restrict the applicability of this approach using UAV platforms. This paper proposes a methodology for determining the geolocation of a target tracked from an aerial platform in a partially GPS devoid environment. The method utilises automatic feature based registration technique of a georeferenced satellite image with an ae rial image which is already stored in UAV's database to retrieve the geolocation of the target. Since it is easier to register subsequent aerial images due to similar viewing parameters, the subsequent overlapping images are registered together sequentially thus resulting in the registration of each of the images with georeferenced satellite image thus leading to geolocation of the target under interest. Using the proposed approach, the target can be tracked in all the frames in which it is visible. The proposed concept is verified experimentally and the results are found satisfactory. Using the proposed method, a user can obtain location of target of interest as well features on ground without requiring any POS on-board the aerial platform. The proposed approach has applications in surveillance for target tracking, target geolocation as well as in disaster management projects like search and rescue operations.
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Shukla, P. K., S. Goel, P. Singh, and B. Lohani. "Automatic geolocation of targets tracked by aerial imaging platforms using satellite imagery." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-8 (November 28, 2014): 1213–20. http://dx.doi.org/10.5194/isprsarchives-xl-8-1213-2014.
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Tracking of targets from aerial platforms is an important activity in several applications, especially surveillance. Knowled ge of geolocation of these targets adds additional significant and useful information to the application. This paper determines the geolocation of a target being tracked from an aerial platform using the technique of image registration. Current approaches utilize a POS to determine the location of the aerial platform and then use the same for geolocation of the targets using the principle of photogrammetry. The constraints of cost and low-payload restrict the applicability of this approach using UAV platforms. This paper proposes a methodology for determining the geolocation of a target tracked from an aerial platform in a partially GPS devoid environment. The method utilises automatic feature based registration technique of a georeferenced satellite image with an ae rial image which is already stored in UAV's database to retrieve the geolocation of the target. Since it is easier to register subsequent aerial images due to similar viewing parameters, the subsequent overlapping images are registered together sequentially thus resulting in the registration of each of the images with georeferenced satellite image thus leading to geolocation of the target under interest. Using the proposed approach, the target can be tracked in all the frames in which it is visible. The proposed concept is verified experimentally and the results are found satisfactory. Using the proposed method, a user can obtain location of target of interest as well features on ground without requiring any POS on-board the aerial platform. The proposed approach has applications in surveillance for target tracking, target geolocation as well as in disaster management projects like search and rescue operations.
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Huang, Xianyun, Songxiao Cao, Chenguang Dong, Tao Song, and Zhipeng Xu. "Improved Fully Convolutional Siamese Networks for Visual Object Tracking Based on Response Behaviour Analysis." Sensors 22, no. 17 (August 30, 2022): 6550. http://dx.doi.org/10.3390/s22176550.
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Siamese networks have recently attracted significant attention in the visual tracking community due to their balanced accuracy and speed. However, as a result of the non-update of the appearance model and the changing appearance of the target, the problem of tracking drift is a regular occurrence, particularly in background clutter scenarios. As a means of addressing this problem, this paper proposes an improved fully convolutional Siamese tracker that is based on response behaviour analysis (SiamFC-RBA). Firstly, the response map of the SiamFC is normalised to an 8-bit grey image, and the isohypse contours that represent the candidate target region are generated through thresholding. Secondly, the dynamic behaviour of the contours is analysed in order to check if there are distractors approaching the tracked target. Finally, a peak switching strategy is used as a means of determining the real tracking position of all candidates. Extensive experiments conducted on visual tracking benchmarks, including OTB100, GOT-10k and LaSOT, demonstrated that the proposed tracker outperformed the compared trackers such as DaSiamRPN, SiamRPN, SiamFC, CSK, CFNet and Staple and achieved state-of-the-art performance. In addition, the response behaviour analysis module was embedded into DiMP, with the experimental results showing the performance of the tracker to be improved through the use of the proposed architecture.
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Ito, Masanori, Ikuo Matsuo, Tomohito Imaizumi, Tomonari Akamatsu, Yong Wang, and Yasushi Nishimori. "Target strength spectra of tracked individual fish in schools." Fisheries Science 81, no. 4 (May 29, 2015): 621–33. http://dx.doi.org/10.1007/s12562-015-0890-7.
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Sun, Lifan, Jinjin Zhang, Zhe Yang, and Bo Fan. "A Motion-Aware Siamese Framework for Unmanned Aerial Vehicle Tracking." Drones 7, no. 3 (February 22, 2023): 153. http://dx.doi.org/10.3390/drones7030153.
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In recent years, visual tracking has been employed in all walks of life. The Siamese trackers formulate the tracking problem as a template-matching process, and most of them can meet the real-time requirements, making them more suitable for UAV tracking. Because existing trackers can only use the first frame of a video sequence as a reference, the appearance of the tracked target will change when an occlusion, fast motion, or similar target appears, resulting in tracking drift. It is difficult to recover the tracking process once the drift phenomenon occurs. Therefore, we propose a motion-aware Siamese framework to assist Siamese trackers in detecting tracking drift over time. The base tracker first outputs the original tracking results, after which the drift detection module determines whether or not tracking drift occurs. Finally, the corresponding tracking recovery strategies are implemented. More stable and reliable tracking results can be obtained using the Kalman filter’s short-term prediction ability and more effective tracking recovery strategies to avoid tracking drift. We use the Siamese region proposal network (SiamRPN), a typical representative of an anchor-based algorithm, and Siamese classification and regression (SiamCAR), a typical representative of an anchor-free algorithm, as the base trackers to test the effectiveness of the proposed method. Experiments were carried out on three public datasets: UAV123, UAV20L, and UAVDT. The modified trackers (MaSiamRPN and MaSiamCAR) both outperformed the base tracker.
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Ma, Rui, He Cui, Sang-Hun Lee, Thomas J. Anastasio, and Joseph G. Malpeli. "Predictive encoding of moving target trajectory by neurons in the parabigeminal nucleus." Journal of Neurophysiology 109, no. 8 (April 15, 2013): 2029–43. http://dx.doi.org/10.1152/jn.01032.2012.
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Intercepting momentarily invisible moving objects requires internally generated estimations of target trajectory. We demonstrate here that the parabigeminal nucleus (PBN) encodes such estimations, combining sensory representations of target location, extrapolated positions of briefly obscured targets, and eye position information. Cui and Malpeli (Cui H, Malpeli JG. J Neurophysiol 89: 3128–3142, 2003) reported that PBN activity for continuously visible tracked targets is determined by retinotopic target position. Here we show that when cats tracked moving, blinking targets the relationship between activity and target position was similar for ON and OFF phases (400 ms for each phase). The dynamic range of activity evoked by virtual targets was 94% of that of real targets for the first 200 ms after target offset and 64% for the next 200 ms. Activity peaked at about the same best target position for both real and virtual targets. PBN encoding of target position takes into account changes in eye position resulting from saccades, even without visual feedback. Since PBN response fields are retinotopically organized, our results suggest that activity foci associated with real and virtual targets at a given target position lie in the same physical location in the PBN, i.e., a retinotopic as well as a rate encoding of virtual-target position. We also confirm that PBN activity is specific to the intended target of a saccade and is predictive of which target will be chosen if two are offered. A Bayesian predictor-corrector model is presented that conceptually explains the differences in the dynamic ranges of PBN neuronal activity evoked during tracking of real and virtual targets.
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Fan, Heng, Jinhai Xiang, Jun Xu, and Honghong Liao. "Part-Based Visual Tracking via Online Weighted P-N Learning." Scientific World Journal 2014 (2014): 1–13. http://dx.doi.org/10.1155/2014/402185.
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We propose a novel part-based tracking algorithm using online weighted P-N learning. An online weighted P-N learning method is implemented via considering the weight of samples during classification, which improves the performance of classifier. We apply weighted P-N learning to track a part-based target model instead of whole target. In doing so, object is segmented into fragments and parts of them are selected as local feature blocks (LFBs). Then, the weighted P-N learning is employed to train classifier for each local feature block (LFB). Each LFB is tracked through the corresponding classifier, respectively. According to the tracking results of LFBs, object can be then located. During tracking process, to solve the issues of occlusion or pose change, we use a substitute strategy to dynamically update the set of LFB, which makes our tracker robust. Experimental results demonstrate that the proposed method outperforms the state-of-the-art trackers.
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Tang, Cundong, Li Chen, Yi Wang, Wusi Yang, Rui Chen, and Zhiping Wang. "The Role of 5G Network Image Information Based on Deep Learning in UAV Prediction Target Trajectory Tracking." Wireless Communications and Mobile Computing 2021 (December 11, 2021): 1–13. http://dx.doi.org/10.1155/2021/3097031.
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With the development of information technology in the network era and the popularization of the 5G era, UAV-related applications are becoming more and more widely used, which is one of the essential basic technologies. Therefore, the technology has great research value and practical significance, a multiobjective detector based on support vector machine (SVM) is designed based on directional gradient histogram (HOG), and the startup method used with cross-validation methods can improve detector performance. It makes the detector accuracy above 98% and has good resistance to the target scale. A real-time target tracker is designed with its rotation variation and with an improved average displacement algorithm. The algorithm must manually select the target model and suggest the target model to achieve automatic acquisition of the target model. Due to the ambiguity of the target tracking state, several judgment conditions are set to determine whether the tracking has failed and whether the tracker state is correctly verified, with several similar target tracking algorithms. When the system is started, the system detects targets frame by frame. And it will locate a possible target by color segmentation and specify the target to be tracked to recommend the relevant model during the tracking process and open the tracker to determine the target tracking state frame by frame and perform target detection at each frame. Then it will find possible goals and will follow them to achieve a balance of stable and real-time system performance, using the results of the TPD-KCF method. The percentage of correctly tracking images can reach 98%, and the efficiency is significantly improved.
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De Coninck, Bert, Jan Victor, Patrick De Baets, Stijn Herregodts, and Matthias Verstraete. "Design optimisation for optically tracked pointers." International Journal Sustainable Construction & Design 8, no. 1 (October 30, 2017): 10. http://dx.doi.org/10.21825/scad.v8i1.6805.
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The use of mechanical pointers in optical tracking systems is needed to aid registration processes of unlocated rigid bodies. Error on the target point of a pointer can cause wrong positioning of vital objects and as such these errors have to be avoided. In this paper, the different errors that originate during this process are described, after which this error analysis is used for the optimisation of an improved pointer design. The final design contains six coplanar fiducials, favored by its robustness and low error. This configuration of fiducials is then analysed theoretically as well as practically to understand how it is performing. The error on tracking the target point of the pointer is found with simulation to be around 0.7 times the error of measuring one fiducial in space. However, practically this error is about equal to the fiducial tracking error, due to the non-normally distributed errors on each separate fiducial.
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Judge, S. J., and B. G. Cumming. "Neurons in the monkey midbrain with activity related to vergence eye movement and accommodation." Journal of Neurophysiology 55, no. 5 (May 1, 1986): 915–30. http://dx.doi.org/10.1152/jn.1986.55.5.915.
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We recorded from neurons dorsal and dorsolateral to the third nerve nucleus of the monkey whose discharge rates modulated when the monkey tracked targets moving in depth but not when it tracked targets moving from side to side. The neurons' activity modulated equally well whether the target moved directly toward one eye or the other. For most neurons the amplitude of modulation was similar whether the monkey tracked monocularly (blur cue alone), binocularly with accommodation open-loop (disparity cue alone), or in normal binocular viewing. By comparing the modulation in normal binocular viewing with that when the blur and disparity cues were in conflict we were able to show that 19 neurons discharged in relation to the vergence response alone and not to accommodation. Eight neurons discharged exclusively in relation to accommodation. While the monkeys tracked targets moving in depth so that target vergence varied with a sinusoidal time course (frequency 0.1 or 0.2 Hz) the discharge modulations of identified vergence cells generally showed much more phase lead than expected of motoneurons. We examined the activity of a subset of these vergence cells in response to a range of stimulus frequencies to compare the dynamics of these neurons with motoneurons. The phase leads were larger than those expected of motoneurons over the entire frequency range tested. We speculate that vergence neurons may selectively activate (directly or indirectly) motoneurons with longer time constants than the mean.
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Yuan, Gang, Fengkun Lv, Zhihao Xu, Lv Luo, Yi Rong, Yanan Zhang, and Zheng Tian. "Research on face tracking Algorithm Based on Detection and Supervision Tracking." Journal of Physics: Conference Series 2209, no. 1 (February 1, 2022): 012028. http://dx.doi.org/10.1088/1742-6596/2209/1/012028.
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Abstract Accurate face detection and tracking is widely used in many social life scenes. However, the uncontrollable background noise and random illumination change in the application scene will reduce the detection accuracy of the tracked target, and the rotation, occlusion and overlap of the tracked target will also affect the accuracy and success rate of the tracking algorithm. In order to solve the above problems, this paper proposes a method that uses deep learning detection method to supervise the correlation filter tracking algorithm to improve the success rate of de-tection and tracking. First of all, in the first frame of the picture, we use the deep learning SSD (Single Shot MultiBox Detector) algorithm to detect the face, and take the detected face as the tracking target, and use the correlation filtering algorithm DSST (Discriminative Scale Space Tracker) Tracking in the process of tracking, face detection is continuously carried out on the tracking target, and the detection results are used to monitor the tracking results, so as to reduce the target drift caused by the boundary effect, thus improving the accuracy of the tracking algorithm. The algorithm is tested and verified on OTB100 data set. The final experimental results show that the accuracy of this algorithm is obviously better than the mainstream classical algorithm, and the frame rate meets the real-time re-quirements.
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Chuang, Cynthia, Arjun Sahgal, Letitia Lee, David Larson, Kim Huang, Paula Petti, Lynn Verhey, and Lijun Ma. "Effects of residual target motion for image-tracked spine radiosurgery." Medical Physics 34, no. 11 (October 29, 2007): 4484–90. http://dx.doi.org/10.1118/1.2790587.
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Stateczny, Andrzej. "Neural Manoeuvre Detection of the Tracked Target in ARPA Systems." IFAC Proceedings Volumes 34, no. 7 (July 2001): 209–14. http://dx.doi.org/10.1016/s1474-6670(17)35084-x.
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Yang, Xiaqing, Jun Shi, Yuanyuan Zhou, Chen Wang, Yao Hu, Xiaoling Zhang, and Shunjun Wei. "Ground Moving Target Tracking and Refocusing Using Shadow in Video-SAR." Remote Sensing 12, no. 18 (September 20, 2020): 3083. http://dx.doi.org/10.3390/rs12183083.
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Stable and efficient ground moving target tracking and refocusing is a hard task in synthetic aperture radar (SAR) data processing. Since shadows in video-SAR indicate the actual positions of moving targets at different moments without any displacement, shadow-based methods provide a new approach for ground moving target processing. This paper constructs a novel framework to refocus ground moving targets by using shadows in video-SAR. To this end, an automatic-registered SAR video is first obtained using the video-SAR back-projection (v-BP) algorithm. The shadows of multiple moving targets are then tracked using a learning-based tracker, and the moving targets are ultimately refocused via a proposed moving target back-projection (m-BP) algorithm. With this framework, we can perform detecting, tracking, imaging for multiple moving targets integratedly, which significantly improves the ability of moving-target surveillance for SAR systems. Furthermore, a detailed explanation of the shadow of a moving target is presented herein. We find that the shadow of ground moving targets is affected by a target’s size, radar pitch angle, carrier frequency, synthetic aperture time, etc. With an elaborate system design, we can obtain a clear shadow of moving targets even in X or C band. By numerical experiments, we find that a deep network, such as SiamFc, can easily track shadows and precisely estimate the trajectories that meet the accuracy requirement of the trajectories for m-BP.
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Li, Jing, Xiaodong Ma, Huaimin Chen, Xiaojun Duan, and Yanlong Zhang. "Real-time Detection and Tracking Method of Landmark Based on UAV Visual Navigation." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 36, no. 2 (April 2018): 294–301. http://dx.doi.org/10.1051/jnwpu/20183620294.
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A real-time detection and tracking method of landmark based for UAV visual navigation and fixed landing was proposed. This method used the SVM classification algorithm to train the offline classifier based on SURF-BoW features extracted from samples, which can recognize the landing landmark accurately and complete the initialization of the tracker. Afterwards, tracked the landmark via the improved median flow algorithm to ensure the reliability and integrity of the tracking target. Finally, based on the classifier and the principle of similarity between two adjacent frames' target, this paper designed a target re-search algorithm to ensure that the target can be retrieved quickly even if the target is lost or the target tracking fails, which makes the entire set of algorithm track the target accurately for a long time. The experimental results show that the proposed algorithm has good tracking performance under the conditions of the change of target scale, illumination changes and motion blur.
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Stubberud, Stephen Craig, Kathleen Ann Kramer, and Allen Roger Stubberud. "Estimation of Target Maneuvers from Tracked Behavior Using Fuzzy Evidence Accrual." Advances in Science, Technology and Engineering Systems Journal 4, no. 4 (2019): 468–77. http://dx.doi.org/10.25046/aj040457.
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Park, Ji Hun. "Volumetric Model Body Outline Computation for an Object Tracking in a Video Stream." Applied Mechanics and Materials 479-480 (December 2013): 897–900. http://dx.doi.org/10.4028/www.scientific.net/amm.479-480.897.
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This paper presents a new outline contour generation method to track a rigid body in single video stream taken using a varying focal length and moving camera. We assume feature points and background eliminated images are provided, and we get different views of a tracked object when the object is stationary. Using different views of a tracked object, we volume-reconstruct a 3D model body after 3D scene analysis. For computing camera parameters and target object movement for a scene with a moving target object, we use fixed feature background points, and convert as a parameter optimization problem solving. Performance index for parameter optimization is minimizing feature point errors as well as outline contour difference between reconstructed 3D model and background eliminated tracked object. The proposed method is tested using an input image set.
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Michalski, Julien, Andrea M. Green, and Paul Cisek. "Reaching decisions during ongoing movements." Journal of Neurophysiology 123, no. 3 (March 1, 2020): 1090–102. http://dx.doi.org/10.1152/jn.00613.2019.
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Neurophysiological studies suggest that when decisions are made between concrete actions, the selection process involves a competition between potential action representations in the same sensorimotor structures involved in executing those actions. However, it is unclear how such models can explain situations, often encountered during natural behavior, in which we make decisions while were are already engaged in performing an action. Does the process of deliberation characterized in classical studies of decision-making proceed the same way when subjects are deciding while already acting? In the present study, human subjects continuously tracked a target moving in the horizontal plane and were occasionally presented with a new target to which they could freely choose to switch at any time, whereupon it became the new tracked target. We found that the probability of choosing to switch increased with decreasing distance to the new target and increasing size of the new target relative to the tracked target, as well as when the direction to the new target was aligned (either toward or opposite) to the current tracking direction. However, contrary to our expectations, subjects did not choose targets that minimized the energetic costs of execution, as calculated by a biomechanical model of the arm. When the constraints of continuous tracking were removed in variants of the task involving point-to-point movements, the expected preference for lower cost choices was seen. These results are discussed in the context of current theories of nested feedback control, internal models of forward dynamics, and high-dimensional neural spaces. NEW & NOTEWORTHY Current theories of decision-making primarily address how subjects make decisions before executing selected actions. However, in our daily lives we often make decisions while already performing some action (e.g., while playing a sport or navigating through a crowd). To gain insight into how current theories can be extended to such “decide-while-acting” scenarios, we examined human decisions during continuous manual tracking and found some intriguing departures from how decisions are made in classical “decide-then-act” paradigms.
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Yang, Honghong, and Shiru Qu. "Online Hierarchical Sparse Representation of Multifeature for Robust Object Tracking." Computational Intelligence and Neuroscience 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/5894639.
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Object tracking based on sparse representation has given promising tracking results in recent years. However, the trackers under the framework of sparse representation always overemphasize the sparse representation and ignore the correlation of visual information. In addition, the sparse coding methods only encode the local region independently and ignore the spatial neighborhood information of the image. In this paper, we propose a robust tracking algorithm. Firstly, multiple complementary features are used to describe the object appearance; the appearance model of the tracked target is modeled by instantaneous and stable appearance features simultaneously. A two-stage sparse-coded method which takes the spatial neighborhood information of the image patch and the computation burden into consideration is used to compute the reconstructed object appearance. Then, the reliability of each tracker is measured by the tracking likelihood function of transient and reconstructed appearance models. Finally, the most reliable tracker is obtained by a well established particle filter framework; the training set and the template library are incrementally updated based on the current tracking results. Experiment results on different challenging video sequences show that the proposed algorithm performs well with superior tracking accuracy and robustness.
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Yang, Guosheng, and Qisheng Wei. "Visual Object Multimodality Tracking Based on Correlation Filters for Edge Computing." Security and Communication Networks 2020 (December 10, 2020): 1–13. http://dx.doi.org/10.1155/2020/8891035.
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In recent years, visual object tracking has become a very active research field which is mainly divided into the correlation filter-based tracking and deep learning (e.g., deep convolutional neural network and Siamese neural network) based tracking. For target tracking algorithms based on deep learning, a large amount of computation is required, usually deployed on expensive graphics cards. However, for the rich monitoring devices in the Internet of Things, it is difficult to capture all the moving targets in each device in real time, so it is necessary to perform hierarchical processing and use tracking based on correlation filtering in insensitive areas to alleviate the local computing pressure. In sensitive areas, upload the video stream to a cloud computing platform with a faster computing speed to perform an algorithm based on deep features. In this paper, we mainly focus on the correlation filter-based tracking. In the correlation filter-based tracking, the discriminative scale space tracker (DSST) is one of the most popular and typical ones which is successfully applied to many application fields. However, there are still some improvements that need to be further studied for DSST. One is that the algorithms do not consider the target rotation on purpose. The other is that it is a very heavy computational load to extract the histogram of oriented gradient (HOG) features from too many patches centered at the target position in order to ensure the scale estimation accuracy. To address these two problems, we introduce the alterable patch number for target scale tracking and the space searching for target rotation tracking into the standard DSST tracking method and propose a visual object multimodality tracker based on correlation filters (MTCF) to simultaneously cope with translation, scale, and rotation in plane for the tracked target and to obtain the target information of position, scale, and attitude angle at the same time. Finally, in Visual Tracker Benchmark data set, the experiments are performed on the proposed algorithms to show their effectiveness in multimodality tracking.
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Sun, Yamin, Yue Zhao, and Sirui Wang. "Multiple Traffic Target Tracking with Spatial-Temporal Affinity Network." Computational Intelligence and Neuroscience 2022 (May 23, 2022): 1–13. http://dx.doi.org/10.1155/2022/9693767.
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Traffic target tracking is a core task in intelligent transportation system because it is useful for scene understanding and vehicle autonomous driving. Most state-of-the-art (SOTA) multiple object tracking (MOT) methods adopt a two-step procedure: object detection followed by data association. The object detection has made great progress with the development of deep learning. However, the data association still heavily depends on hand crafted constraints, such as appearance, shape, and motion, which need to be elaborately trained for a special object. In this study, a spatial-temporal encoder-decoder affinity network is proposed for multiple traffic targets tracking, aiming to utilize the power of deep learning to learn a robust spatial-temporal affinity feature of the detections and tracklets for data association. The proposed spatial-temporal affinity network contains a two-stage transformer encoder module to encode the features of the detections and the tracked targets at the image level and the tracklet level, aiming to capture the spatial correlation and temporal history information. Then, a spatial transformer decoder module is designed to compute the association affinity, where the results from the two-stage transformer encoder module are fed back to fully capture and encode the spatial and temporal information from the detections and the tracklets of the tracked targets. Thus, efficient affinity computation can be applied to perform data association in online tracking. To validate the effectiveness of the proposed method, three popular multiple traffic target tracking datasets, KITTI, UA-DETRAC, and VisDrone, are used for evaluation. On the KITTI dataset, the proposed method is compared with 15 SOTA methods and achieves 86.9% multiple object tracking accuracy (MOTA) and 85.71% multiple object tracking precision (MOTP). On the UA-DETRAC dataset, 12 SOTA methods are used to compare with the proposed method, and the proposed method achieves 20.82% MOTA and 35.65% MOTP, respectively. On the VisDrone dataset, the proposed method is compared with 10 SOTA trackers and achieves 40.5% MOTA and 74.1% MOTP, respectively. All those experimental results show that the proposed method is competitive to the state-of-the-art methods by obtaining superior tracking performance.
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Chen, Ze Yu, and Guang Yao Zhao. "Fuzzy Control Strategy and Simulation for Dual Electric Tracked Vehicle Motion Control." Applied Mechanics and Materials 130-134 (October 2011): 309–12. http://dx.doi.org/10.4028/www.scientific.net/amm.130-134.309.
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Based on tracked vehicle dynamics analysis, a fuzzy control strategy is proposed in this paper for the dual electric tracked vehicle motion control. The inputs of fuzzy system are driver acceleration, braking and steering signals besides vehicle velocity feedback signal, while outputs are dual motors’ torque commands and mechanical braker’s target force. Control strategy contains two fuzzy logics, one is for steering and straight-line running control, the other is for braking control section. Simulation results show that the fuzzy control strategy presented here is correct and effective for electric tracked vehicle motion control.
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Chen, Yuntao, Bin Wu, guangzhi Luo, xiaoyan Chen, and junlin Liu. "Multi-target tracking algorithm based on YOLO+DeepSORT." Journal of Physics: Conference Series 2414, no. 1 (December 1, 2022): 012018. http://dx.doi.org/10.1088/1742-6596/2414/1/012018.
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Abstract After several years of development, the multi-target tracking algorithm has significantly transitioned from being researched to being put into practical production and life. The application field of human detection and tracking technology is closely related to our daily life. In order to solve the problems of the background complexity, the diversity of object shapes in the application of multi-target algorithms, and the mutual occlusion between multiple tracking targets and the lost target, this paper improves the DeepSORT target tracking algorithm, uses the improved YOLO network to detect pedestrians, inputs the detection frame to the Kalman filter for prediction output, and then uses the Hungarian algorithm to realize a tracking frame and detection frame of the predicted output. The experimental results show that target tracking accuracy is increased by 4.3%, the running time is the shortest, and the number of successfully tracked targets is relatively high.
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Chi, Chia-Fen, and Chia-Liang Lin. "Speed and Accuracy of Eye-Gaze Pointing." Perceptual and Motor Skills 85, no. 2 (October 1997): 705–18. http://dx.doi.org/10.2466/pms.1997.85.2.705.
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The current experiment examined the speed-accuracy trade-off of saccadic movement between two targets. Ten subjects looked alternately at two targets as fast and as accurately as possible for 2 min. under different conditions of target size, distance between targets, and direction of eye movement. Saccadic movement of the left eye was tracked and recorded with an infrared eye monitoring device to compute the starting position, ending position, and duration of each saccadic movement. Eye-movement time was significantly related to target size and distance between targets, but the speed-accuracy trade-off was significantly different from that predicted by Fitts' Law. Reaction time was not significantly changed by the direction of eye movement.
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Taufique, Abu Md Niamul, Breton Minnehan, and Andreas Savakis. "Benchmarking Deep Trackers on Aerial Videos." Sensors 20, no. 2 (January 19, 2020): 547. http://dx.doi.org/10.3390/s20020547.
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In recent years, deep learning-based visual object trackers have achieved state-of-the-art performance on several visual object tracking benchmarks. However, most tracking benchmarks are focused on ground level videos, whereas aerial tracking presents a new set of challenges. In this paper, we compare ten trackers based on deep learning techniques on four aerial datasets. We choose top performing trackers utilizing different approaches, specifically tracking by detection, discriminative correlation filters, Siamese networks and reinforcement learning. In our experiments, we use a subset of OTB2015 dataset with aerial style videos; the UAV123 dataset without synthetic sequences; the UAV20L dataset, which contains 20 long sequences; and DTB70 dataset as our benchmark datasets. We compare the advantages and disadvantages of different trackers in different tracking situations encountered in aerial data. Our findings indicate that the trackers perform significantly worse in aerial datasets compared to standard ground level videos. We attribute this effect to smaller target size, camera motion, significant camera rotation with respect to the target, out of view movement, and clutter in the form of occlusions or similar looking distractors near tracked object.
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Jonikaitis, Donatas, Anna Klapetek, and Heiner Deubel. "Spatial attention during saccade decisions." Journal of Neurophysiology 118, no. 1 (July 1, 2017): 149–60. http://dx.doi.org/10.1152/jn.00665.2016.
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Behavioral measures of decision making are usually limited to observations of decision outcomes. In the present study, we made use of the fact that oculomotor and sensory selection are closely linked to track oculomotor decision making before oculomotor responses are made. We asked participants to make a saccadic eye movement to one of two memorized target locations and observed that visual sensitivity increased at both the chosen and the nonchosen saccade target locations, with a clear bias toward the chosen target. The time course of changes in visual sensitivity was related to saccadic latency, with the competition between the chosen and nonchosen targets resolved faster before short-latency saccades. On error trials, we observed an increased competition between the chosen and nonchosen targets. Moreover, oculomotor selection and visual sensitivity were influenced by top-down and bottom-up factors as well as by selection history and predicted the direction of saccades. Our findings demonstrate that saccade decisions have direct visual consequences and show that decision making can be traced in the human oculomotor system well before choices are made. Our results also indicate a strong association between decision making, saccade target selection, and visual sensitivity. NEW & NOTEWORTHY We show that saccadic decisions can be tracked by measuring spatial attention. Spatial attention is allocated in parallel to the two competing saccade targets, and the time course of spatial attention differs for fast-slow and for correct-erroneous decisions. Saccade decisions take the form of a competition between potential saccade goals, which is associated with spatial attention allocation to those locations.
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GOODIN, D. T., A. NOBILE, N. B. ALEXANDER, L. C. BROWN, J. L. MAXWELL, J. PULSIFER, A. M. SCHWENDT, M. TILLACK, and R. S. WILLMS. "A credible pathway for heavy ion driven target fabrication and injection." Laser and Particle Beams 20, no. 3 (July 2002): 515–20. http://dx.doi.org/10.1017/s0263034602203304.
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The Target Fabrication Facility (TFF) of an inertial fusion energy (IFE) power plant must supply about 500,000 targets per day. The target is injected into the target chamber at a rate of 5–10 Hz and tracked precisely so the heavy ion driver beams can be directed to the target. The feasibility of developing successful fabrication and injection methodologies at the low cost required for energy production (about $0.25/target, approximately 104 times less than current costs) is a critical issue for inertial fusion energy. A significant program is underway to develop the high-volume methods to supply economical IFE targets. This article reviews the requirements for heavy ion driven IFE target fabrication and injection, and presents the current status of and results from the development program. For the first time, an entire pathway from beginning to end is outlined for fabrication of a high-gain, distributed radiator target. A significant development and scale-up program will be necessary to implement this pathway for mass production of IFE targets.
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Yamuna G., Karthika Pragadeeswari C. ,. "RIGID TRACKING FOR SCALE AND ROTATION VARYING TARGETS FROM MOVING CAMERA." INFORMATION TECHNOLOGY IN INDUSTRY 9, no. 2 (March 24, 2021): 175–80. http://dx.doi.org/10.17762/itii.v9i2.327.
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Targets when move rapidly needed to be tracked in many significant fields such as in combat applications. Objects undergoes many scale changes and also undergoes rotation variance. The target when viewed from static position, the size becomes smaller as the target moves farther and farther. Tracking the targets needs more attention and this can be done by Improved optical flow to which feature extraction through Histogram of Oriented Gradients and Random Sample Consensus (RANSAC) algorithm for scale and rotation invariance is added. The performance of the method is measured by its computation time, accuracy and high true positive values and other related parameters simulated in MAT LAB.
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Chadwick, Roger A., and Skye Pazuchanics. "Spatial Disorientation in Remote Ground Vehicle Operations: Target Localization Errors." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 51, no. 4 (October 2007): 161–65. http://dx.doi.org/10.1177/154193120705100404.
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Operating remote unmanned ground vehicles (UGVs) poses the risk of operator spatial disorientation. While the position of the vehicle can be tracked and displayed on a global map, operators face the difficult task of integrating object (target) information from context limited ground views with a global map view. Studies indicate this can be a challenging task. An analysis of specific object location errors is presented.
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Qu, Zhiyi, Xue Zhao, Huihui Xu, Hongying Tang, Jiang Wang, and Baoqing Li. "An Improved Q-Learning-Based Sensor-Scheduling Algorithm for Multi-Target Tracking." Sensors 22, no. 18 (September 15, 2022): 6972. http://dx.doi.org/10.3390/s22186972.
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Target tracking is an essential issue in wireless sensor networks (WSNs). Compared with single-target tracking, how to guarantee the performance of multi-target tracking is more challenging because the system needs to balance the tracking resource for each target according to different target properties and network status. However, the balance of tracking task allocation is rarely considered in those prior sensor-scheduling algorithms, which may result in the degradation of tracking accuracy for some targets and additional system energy consumption. To address this issue, we propose in this paper an improved Q-learning-based sensor-scheduling algorithm for multi-target tracking (MTT-SS). First, we devise an entropy weight method (EWM)-based strategy to evaluate the priority of targets being tracked according to target properties and network status. Moreover, we develop a Q-learning-based task allocation mechanism to obtain a balanced resource scheduling result in multi-target-tracking scenarios. Simulation results demonstrate that our proposed algorithm can obtain a significant enhancement in terms of tracking accuracy and energy efficiency compared with the existing sensor-scheduling algorithms.
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Wu, Fan, Shi Liu, and Wen Jing Mu. "A Tracked Robot for Complex Environment Detecting." Applied Mechanics and Materials 670-671 (October 2014): 1389–92. http://dx.doi.org/10.4028/www.scientific.net/amm.670-671.1389.
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The complex geographical environment put forward grand challenge to most current detecting robot on the problem of communication control and moving obstacle avoidance.This paper proposed a crawler robot for complex environment detecting.Information acquisition was achieved by Labview programming,the collected sensor information was debugged by fusing and displaying in the Labview interface , in order to realize the stability of the system.The positioning part adopted wireless location technology of ZigBee networks,which can support a large number of network nodes, fast, and reliable security etc.In addition, the upper part of the robot body was equipped with camera and mechanical arm to achieve target capture, carrying something and other tasks. According to the situation of accident, remote control terminal through wireless control function can control the further action of the robot. Through experimental tests, this paper proposes this kind of detection robot had great advantages in detection, search and rescue, it can complete the search under complex and dangerous environment.
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Lu, Zhihua, Mengyao Zhu, Qingwei Ye, Yu Zhou, and Lingfu Xie. "Experimental Target Tracking Using Asynchronous Sensors." Wireless Communications and Mobile Computing 2018 (October 8, 2018): 1–7. http://dx.doi.org/10.1155/2018/7059048.
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We investigate the problem of target tracking using a wireless sensor network with asynchronous sensors. To study the impact of sensor clock imperfection on target tracking in practical situations, we build a testbed and collect data from an outdoor experiment. After analyzing the collected data, we find that the TDOA (time-difference-of-arrival) and FDOA (frequency-difference-of-arrival) measurements have notable bias, which is caused by asynchronous sensors or more precisely by the sensor clock drift. Based on the model of clock drift, the measurement bias and the target position are integrated into a state-space model. Both can be estimated in the framework of the extend Kalman filter. In some circumstance, the target trajectory is tracked successfully.
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Fang, Ruoyu, and Cheng Cai. "Computer vision based obstacle detection and target tracking for autonomous vehicles." MATEC Web of Conferences 336 (2021): 07004. http://dx.doi.org/10.1051/matecconf/202133607004.
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Obstacle detection and target tracking are two major issues for intelligent autonomous vehicles. This paper proposes a new scheme to achieve target tracking and real-time obstacle detection of obstacles based on computer vision. ResNet-18 deep learning neural network is utilized for obstacle detection and Yolo-v3 deep learning neural network is employed for real-time target tracking. These two trained models can be deployed on an autonomous vehicle equipped with an NVIDIA Jetson Nano motherboard. The autonomous vehicle moves to avoid obstacles and follow tracked targets by camera. Adjusting the steering and movement of the autonomous vehicle according to the PID algorithm during the movement, therefore, will help the proposed vehicle achieve stable and precise tracking.
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Zhao, Long, Zhen Liu, Tiejun Li, Baoqi Huang, and Lihua Xie. "Moving Target Positioning Based on a Distributed Camera Network." Mathematical Problems in Engineering 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/803743.
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We propose a systematic framework for moving target positioning based on a distributed camera network. In the proposed framework, low-cost static cameras are deployed to cover a large region, moving targets are detected and then tracked using corresponding algorithms, target positions are estimated by making use of the geometrical relationships among those cameras after calibrating those cameras, and finally, for each target, its position estimates obtained from different cameras are unified into the world coordinate system. This system can function as complementary positioning information sources to realize moving target positioning in indoor or outdoor environments when global navigation satellite system (GNSS) signals are unavailable. The experiments are carried out using practical indoor and outdoor environment data, and the experimental results show that the systematic framework and inclusive algorithms are both effective and efficient.
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Zhang, Wenguang, Jizhen Liu, and Deliang Zeng. "Multiple Dynamic Targets Encirclement Control of Multiagent Systems." Mathematical Problems in Engineering 2015 (2015): 1–6. http://dx.doi.org/10.1155/2015/467060.
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This paper develops the distributed encirclement control problem of multiagent systems, in which each agent tracks multiple targets, each target can be tracked by one agent, and the numbers of the agents and the targets are the same or not. Firstly, an encirclement control protocol is proposed for multiagent systems, and this protocol contains some estimators. Secondly, some conditions are derived, under which multiagent systems can achieve encirclement control by circular formation. Finally, numerical simulations are provided to illustrate the obtained results.
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Li, Jianfeng, Amir Khajepour, Yanjun Huang, Hong Wang, Chen Tang, and Yechen Qin. "A new coordinated control strategy for tracked vehicle ride comfort." Proceedings of the Institution of Mechanical Engineers, Part K: Journal of Multi-body Dynamics 232, no. 3 (October 12, 2017): 330–41. http://dx.doi.org/10.1177/1464419317734950.
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To improve tracked vehicle ride comfort and minimize weapon's vibration, a coordinated control strategy is developed for tracked vehicles' semi-active suspension systems. A model with eight degrees-of-freedom for a tracked vehicle equipped with magnetorheological dampers is established, and is followed by the formulation of a sliding mode controller. The proposed control algorithm is a localized-based controller that can change its target location in the tracked vehicle to where it is needed most. A co-simulation system model including a six-wheel tracked vehicle multi-body dynamics model, coordinated control strategy, and magnetorheological damper force allocator is developed to analyze the ride performance under bump and random road excitations. The simulation results demonstrate that the proposed strategy is very effective in improving the vehicle's ride performance and is much better than the traditional skyhook controllers. The innovation of this paper can be concluded as the coordinated control strategy can simultaneously improve vertical acceleration and pitch acceleration for the hull, which is of great importance for combat situations.
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Chen, W. S., J. Liu, and J. Li. "Classification of UAV and bird target in low-altitude airspace with surveillance radar data." Aeronautical Journal 123, no. 1260 (February 2019): 191–211. http://dx.doi.org/10.1017/aer.2018.158.
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ABSTRACTIn order to ensure low-altitude safety, a tracking and recognition method of unmanned aerial vehicle (UAV) and bird targets based on traditional surveillance radar data is proposed. First, several motion models for UAV and flying bird targets are established. Second, the target trajectories are filtered and smoothed with multiple motion models. Third, by calculating the time-domain variance of the model occurrence probability, the model conversion probability of the target is estimated, and then the target type is identified and classified. The effectiveness and robustness of the algorithm is demonstrated by several groups of Monte Carlo simulation experiments, including setting different recognition steps, different model transformation probability, filtering and smoothing algorithm comparison. The algorithm is also successfully applied on the ground-truth radar data collected by the low-altitude surveillance radar at airport and coastal environments, where the targets of UAVs and flying birds could be tracked and recognised.
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Guanghu, Jin, Dong Zhen, Yongsheng Zhang, and Feng He. "Template free Micro Doppler Signature Classification for Wheeled and Tracked Vehicles." Defence Science Journal 69, no. 5 (September 17, 2019): 517–27. http://dx.doi.org/10.14429/dsj.69.12096.
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The micro-Doppler signature is a time-varying frequency modulation imparted on radar echo caused by target’s micro-motion. To save the trouble of constructing template in the target classification, this paper investigates the micro-Doppler signature of wheeled and tracked vehicles and proposes a template-free classification method. Firstly, the echo signature is established and the micro-Doppler difference of these two kinds of targets is analysed. Secondly, some new micro-Doppler features are defined according to their difference. The new defined features are micro-Doppler bandwidth, micro-Doppler expansion rate and micro-Doppler peak number. According to the characteristic of the micro-Doppler in the time-frequency domain, we proposed to realise the feature extraction by Hough transformation. Lastly, template-free subjection functions are proposed to define the relationship between the features and the vehicles. By fuzzy comprehensive evaluation, the final classification result is obtained by combining the subjection probabilities together. Experimental results based on the simulated data and measured data are presented, which prove that the algorithm has good performance.
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Kelemen, Michal, and Bobal Lukas. "TRACKED ROBOT FOR CLEANING OF PIPE." TECHNICAL SCIENCES AND TECHNOLOGIES, no. 3(17) (2019): 55–61. http://dx.doi.org/10.25140/2411-5363-2019-3(17)-55-61.
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Urgency of the research. Service robot is as device which is currently used for standard works as cutting the grass or vacuum cleaning and also teleoperation robot which is remote controlled by human for special tasks as exploring, bomb removing, rescue action etc. Tendency is substitute human in dangerous or monotonous works via using of robots. Target setting. Tracked robot is designed for cleaning of pipe, which has inner pipe wall covered by sediments. The typical example is chimney, where carbon particles cover the pipe wall. Carbon particles can start burning process with result of total damaging of chimney and also building. Pipe robot can be used as practical aid for cleaning and inspection of pipes. Actual scientific researches and issues analysis. Other similar task is repairing of damaged inner pipe wall. Robot which repair pipe from inside pipe saves the costs for site excavation works. Uninvestigated parts of general matters defining. The questions of the design of pipe repairing robots are uninvestigated, because the next research will be focused to this. The research objective. In-pipe robot is as device for locomotion inside pipe with aim to make inspection or cleaning of inner surface of pipe wall. Tracked robot is designed because of better properties as overcoming of problematic places inside pipe and also lower normal force between the tracks and inner pipe wall. The statement of basic materials. Tracked segments are pressed to inner pipe wall and normal force is controlled by controller on the base of measurement of normal force. Cleaning brush module is connected to robot for dirties removing. CCD camera for inspection can be also connected to robot. Conclusions. The cleaning robot is important device for service of pipe systems as prevention of pipe damage of other negative phenomena. Contribution of this robot is significant, because it saves money and time.
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Solovyev, R., A. Kolomeichenko, S. Cheranev, M. Gerasimov, and I. Gribov. "Modular design of diesel-electric tracked tractor with high degree of automation." Journal of Physics: Conference Series 2061, no. 1 (October 1, 2021): 012051. http://dx.doi.org/10.1088/1742-6596/2061/1/012051.
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Abstract The prospective diesel-electric tracked tractor shall have a modular design, integrating the power frame, auxiliary systems, and control system. The tractor will have the following basic modules: a track module comprising an electric motor, gearbox, brake system, electric motor power casing; a diesel generator module consisting of an internal combustion engine of the power corresponding to the tractor’s traction class and a power generator. The article substantiates the need for a diesel-electric tracked tractor with a high degree of automation and unmanned control capability, which will be in demand in modern Digital Agriculture. The stages of technological change in global agriculture are presented. The paper outlines the advantages of tracklaying system and electromechanical transmission; functional diagram and target indicators of some technical characteristics of a diesel-electric tracked tractor with the electromechanical transmission; capabilities and functions of information and control digital, intelligent systems that are to be implemented in a diesel-electric tracked tractor for digital agriculture production.
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Medendorp, W. Pieter, Herbert C. Goltz, and Tutis Vilis. "Directional Selectivity of BOLD Activity in Human Posterior Parietal Cortex for Memory-Guided Double-Step Saccades." Journal of Neurophysiology 95, no. 3 (March 2006): 1645–55. http://dx.doi.org/10.1152/jn.00905.2005.
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We used functional magnetic resonance imaging (fMRI) to investigate the role of the human posterior parietal cortex (PPC) in storing target locations for delayed double-step saccades. To do so, we exploited the laterality of a subregion of PPC that preferentially responds to the memory of a target location presented in the contralateral visual field. Using an event-related design, we tracked fMRI signal changes in this region while subjects remembered the locations of two sequentially flashed targets, presented in either the same or different visual hemifields, and then saccaded to them in sequence. After presentation of the first target, the fMRI signal was always related to the side of the visual field in which it had been presented. When the second target was added, the cortical activity depended on the respective locations of both targets but was still significantly selective for the target of the first saccade. We conclude that this region within the human posterior parietal cortex not only acts as spatial storage center by retaining target locations for subsequent saccades but is also involved in selecting the target for the first intended saccade.
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Han, Jiangyi, Fan Wang, and Yuhang Wang. "A Control Method for the Differential Steering of Tracked Vehicles Driven Independently by a Dual Hydraulic Motor." Applied Sciences 12, no. 13 (June 22, 2022): 6355. http://dx.doi.org/10.3390/app12136355.
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It is well known that tracked vehicles can adapt well to all kinds of terrain. However, the safety of tracked vehicles should be considered during steering on sloped terrain. This paper focuses on the differential steering control of tracked vehicles independently driven by a hydraulic motor. Firstly, the dynamic model of hydrostatic drive system was built and the kinematics and dynamics of differential steering driving were analyzed theoretically. Secondly, in order to prevent rollover of the tracked vehicle, the method of vehicle speed constraint was proposed. The constraint conditions of vehicle speed and steering angular velocity were analyzed under different slope conditions. Thirdly, based on the analysis results, differential steering control rules for tracked vehicles were formulated. To verify the effectiveness of the control rules, the models of vehicle driving dynamics and Fuzzy PID control simulation were established in MATLAB/Simulink. Longitudinal steering simulation was carried out on a slope (0°, 30°), and an analysis of the simulation of lateral steering along the contour line was carried out. The simulation results showed that this steering control strategy was able to automatically adjust the target vehicle speed to avoid rollover while the driver was inputting steering signals.
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Chin, Jih-Hua, and Hu-Wai Lin. "The Algorithms of the Cross-Coupled Precompensation Method for Generating the Involute-Type Scrolls." Journal of Dynamic Systems, Measurement, and Control 121, no. 1 (March 1, 1999): 96–104. http://dx.doi.org/10.1115/1.2802447.
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Cross-coupled precompensation method (CCPM) has been proven efficient in tracking nonlinear spatial curves. It requires a path generating algorithm derived from the mathematical equation of the target curve. This paper discussed the time base transform of target curve from a parametric form. The time based path generating algorithm for the extended involute scroll was then proposed. A comparison among three kinds of tool path generating algorithms were performed. The proposed path algorithms, along with other two algorithms, were implemented and tracked by four different control schemes, US (uncoupled-unprecompensated system), CCS (cross-coupled system), PPM (path precompensation method), and cross-coupled precompensation method (CCPM). The proposed path algorithm for extended involute scroll provided the best accuracy. The proposed algorithm tracked by CCPM achieved the most precise profile especially at high feedrates.
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Hashemzadeh, Farzad. "Asymmetrical Gating with Application on Maneuvering Target Tracking." Applied Computational Intelligence and Soft Computing 2012 (2012): 1–8. http://dx.doi.org/10.1155/2012/670485.
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A new asymmetrical gate with application in target tracking is proposed. Proposed gate has asymmetric shape that has large probability of target detection in the gate and has more advantages compared with elliptical gate. The gate is defined as the region in which the tracked target is expected to exist and just observation vectors in the gate are used as target detection. An analytical method to compute optimal size of gate is proposed and recursive estimation of asymmetric parameters of gate are studied. Comparison between proposed gate and conventional elliptical gate showed the efficiency of the proposed method in maneuvering target tracking applications and simulation results showed the proficiency of the proposed method.
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Chebrolu, Venkata V., Daniel Saenz, Dinesh Tewatia, William A. Sethares, George Cannon, and Bhudatt R. Paliwal. "Rapid Automated Target Segmentation and Tracking on 4D Data without Initial Contours." Radiology Research and Practice 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/547075.
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Purpose. To achieve rapid automated delineation of gross target volume (GTV) and to quantify changes in volume/position of the target for radiotherapy planning using four-dimensional (4D) CT.Methods and Materials. Novel morphological processing and successive localization (MPSL) algorithms were designed and implemented for achieving autosegmentation. Contours automatically generated using MPSL method were compared with contours generated using state-of-the-art deformable registration methods (usingElastix©and MIMVista software). Metrics such as the Dice similarity coefficient, sensitivity, and positive predictive value (PPV) were analyzed. The target motion tracked using the centroid of the GTV estimated using MPSL method was compared with motion tracked using deformable registration methods.Results. MPSL algorithm segmented the GTV in 4DCT images in27.0±11.1seconds per phase (512×512resolution) as compared to142.3±11.3seconds per phase for deformable registration based methods in 9 cases. Dice coefficients between MPSL generated GTV contours and manual contours (considered as ground-truth) were0.865±0.037. In comparison, the Dice coefficients between ground-truth and contours generated using deformable registration based methods were 0.909 ± 0.051.Conclusions. The MPSL method achieved similar segmentation accuracy as compared to state-of-the-art deformable registration based segmentation methods, but with significant reduction in time required for GTV segmentation.
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Li, Changrui, and Qiuping Peng. "Multitarget Tracking Algorithm in Intelligent Analysis of Football Movement Training Stance." Security and Communication Networks 2022 (August 4, 2022): 1–8. http://dx.doi.org/10.1155/2022/6579066.
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In recent years, with the continuous development of computer technology, deep learning has been widely applied to computer vision tasks and has achieved great success in areas such as visual detection and tracking. On this basis, making deep learning techniques truly accessible to people becomes the next objective. Target detection and tracking in football gesture training is a quite challenging task with great practical and commercial value. In traditional football training methods, target trajectories are often extracted by means of a recording chip carried by the player. However, the cost of this method is high and it is difficult to replicate in amateur stadiums. Some studies have also used only cameras to process targets in football videos. However, due to the similarity in appearance and frequent occlusion of targets in football videos, these methods often only segment targets such as players and balls in the image but do not allow them to be tracked. Target tracking techniques are of great importance in football training and are the basis for tasks such as player training analysis and match strategy development. In recent years, many excellent algorithms have emerged in the field of target tracking, mainly in the categories of correlation filtering and deep learning, but none of them are able to achieve high accuracy in player tracking for football training videos. After all, the problem of locating clips of interest to athletes from a full-length video is a pressing one. Traditional machine learning-based approaches to sports event detection have poor accuracy and are limited in the types of events they can detect. These traditional methods often rely on auxiliary information such as audio commentary and relevant text, which are less stable than video. In recent years, deep learning-based methods have made great progress in the detection of single-player video events and actions, but less so in the detection of sports video events. As a result, there are few sports video datasets that can be used for deep learning training. Based on research in computer vision and deep learning, this paper designs a multitarget tracking system for football training. To be specific, this algorithm uses multiple cameras for image acquisition in the stadium in order to accurately track multiple targets in the stadium over time. Furthermore, the framework for a single camera multitarget tracking approach has been designed based on deep learning-based visual detection methods and correlation filter-based tracking methods. This framework focuses on using data correlation algorithms to fuse the results of detectors and trackers so that multiple targets can be tracked accurately in a single camera. To sum up, this research allows for robust and real-time long-term accurate tracking of targets in football training videos through multitarget tracking algorithms and the intercorrection of multiple camera systems.
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Garcia, Luis, Uwe Bielke, Cornelius Neumann, and Rainer Börret. "Machine Learning Based Position Prediction of a Target Tracked by Multi-Aperture Positioning System." International Journal of Automation Technology 17, no. 3 (May 5, 2023): 305–13. http://dx.doi.org/10.20965/ijat.2023.p0305.
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This paper proposes a machine learning-based position prediction approach to determine the position of a light-emitting diode (LED) target using a new measuring system called the multi-aperture positioning system (MAPS). The measurement system is based on a photogrammetric approach using an aperture mask and a single camera sensor. To achieve high accuracy in position calculation, several complex algorithms with high computational complexity are used. The accuracy of the system is equal to or better than that of existing photogrammetric devices. We investigate whether a neural network (NN) can replace the algorithms currently used in the system software to increase the measurement frequency with similar accuracy. Simulated images are used to train the NN, while real images are used to measure performance. Previously, various algorithms were used to calculate the position of the target from the captured images. Our approach is to train an NN, using thousands of labeled images, to predict the position of the target from these images. We investigate whether systematic measurement errors can be avoided; not all factors affecting the measurement precision are yet known, can always be accurately determined, or change over time. When NNs are used, all information contained in the images is learned by the model, considering all influences present at the time of training. Results show that the trained NN can achieve similar performance to the previously used Gaussian algorithm in less time since no filters or other pre-processing of images are required. This factor directly affects the measurement frequency of the MAPS. The light spot center was detected with sub-pixel accuracy without systematic errors in contrast to some of the previously used algorithms. The simulation of the sensor images needs to be improved to investigate the full potential of the NN.