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1
Ling, Jiankun. "Target Tracking Using Kalman Filter Based Algorithms." Journal of Physics: Conference Series 2078, no. 1 (November 1, 2021): 012020. http://dx.doi.org/10.1088/1742-6596/2078/1/012020.
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Abstract Kalman filter and its families have played an important role in information gathering, such as target tracking. Data association techniques have also been developed to allow the Kalman filter to track multiple targets simultaneously. This paper revisits the principle and applications of the Kalman filter for single target tracking and multiple hypothesis tracking (MHT) for multiple target tracking. We present the brief review of the Bayes filter family and introduce a brief derivation of the Kalman filter and MHT. We show examples for both single and multiple targets tracking in simulation to illustrate the efficacy of Kalman filter-based algorithms in target tracking scenarios.
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ZhongMing Liao and Azlan Ismail. "Performance of Correlational Filtering and Deep Learning Based Single Target Tracking Algorithms." Journal of Smart Science and Technology 3, no. 1 (March 30, 2023): 63–79. http://dx.doi.org/10.24191/jsst.v3i1.42.
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Visual target tracking is an important research element in the field of computer vision. The applications are very wide. In terms of the computer vision field, deep learning has achieved remarkable results. It has broken through many complex problems that are difficult to be solved by traditional algorithms. Therefore, reviewing the visual target tracking algorithms based on deep learning from different perspectives is important. This paper closely follows the tracking framework of target tracking algorithms and discusses in detail the traditional visual target tracking methods, the mainstream single target tracking algorithms based on correlation filtering, and the video single target tracking algorithms based on deep learning. Experiments were conducted on OTB100 and VOT2018 benchmark datasets, and the experimental data obtained were analysed to derive two visual single-target tracking algorithms with optimal tracking performance. Finally, the future development of tracking algorithms is envisioned.
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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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Zhang, Haozheng, Xiong Li, and Yu Meng. "Performance Study of Two Bearings-only Target Tracking Algorithms." Journal of Physics: Conference Series 2419, no. 1 (January 1, 2023): 012086. http://dx.doi.org/10.1088/1742-6596/2419/1/012086.
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Abstract For the bearings-only target tracking for single array, the tracking performance of extended kalman filter algorithm in cartesian coordinates and modified polar coordinates is studied. The result shows that the performance of extended kalman filter algorithm in polar coordinates is more general than that in cartesian coordinates. In addition, the tracking performance of these two algorithms decreases with an increase in azimuth measurement error.
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Yuan, Xianghui, Feng Lian, and Chongzhao Han. "Models and Algorithms for Tracking Target with Coordinated Turn Motion." Mathematical Problems in Engineering 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/649276.
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Tracking target with coordinated turn (CT) motion is highly dependent on the models and algorithms. First, the widely used models are compared in this paper—coordinated turn (CT) model with known turn rate, augmented coordinated turn (ACT) model with Cartesian velocity, ACT model with polar velocity, CT model using a kinematic constraint, and maneuver centered circular motion model. Then, in the single model tracking framework, the tracking algorithms for the last four models are compared and the suggestions on the choice of models for different practical target tracking problems are given. Finally, in the multiple models (MM) framework, the algorithm based on expectation maximization (EM) algorithm is derived, including both the batch form and the recursive form. Compared with the widely used interacting multiple model (IMM) algorithm, the EM algorithm shows its effectiveness.
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Wei, Hao, Zong-ping Cai, Bin Tang, and Ze-xiang Yu. "Review of the algorithms for radar single target tracking." IOP Conference Series: Earth and Environmental Science 69 (June 2017): 012073. http://dx.doi.org/10.1088/1755-1315/69/1/012073.
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Zhang, Ming, Li Wang, Hai Hua Shi, and Wei Xiang. "The Target Tracking Algorithm Research of Independent Vision Robot Fish." Advanced Materials Research 753-755 (August 2013): 2015–19. http://dx.doi.org/10.4028/www.scientific.net/amr.753-755.2015.
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In the independent vision robot fish games, the interference of water wave often causes tracking inaccuracy and target tracking failure. In order to solve these problems, the Meanshift algorithm and the combination of Meanshift algorithm and Kalman filter respectively are studied to realize target tracking of independent vision robot fish in this paper. By comparing the two algorithms, the results show that: the former tracking algorithm is not ideal and easy to lose the target. The combined algorithm of Meanshift and Kalman filter can effectively improve the performance of single-target tracking in a complex environment to achieve the goal of continuous accurate tracking.
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Chang-Jian Wang, Chang-Jian Wang, Yong Ding Chang-Jian Wang, and Ye Ji Yong Ding. "An Improved Kernel Correlation Filter Tracking Combined with Mobilenet SSD." 電腦學刊 33, no. 2 (April 2022): 069–81. http://dx.doi.org/10.53106/199115992022043302006.
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<p>This article mainly solves the problems that exist when using the Kernel Correlation Filter (KCF) for tracking in complex scenarios. To make the algorithm suitable for target tracking under complex conditions such as scale changes, similar interference, and occlusion, a MobileNet SSD (Single Shot Detection) target detection combined with an improved KCF target tracking algorithm is proposed. Firstly, the MobileNet SSD is used to locate the target in the initial frame, and the location is sent to KCF for training. Secondly, aiming at the problem of scale changes, a Binary-Tree scale search strategy is proposed. In this strategy, the scale value is searched in a tree shape according to the response size, which reduces the number of scale searches. Finally, the average peak correlation energy is used for occlusion determination, and the model update strategy is improved, thereby enhancing the algorithm’s ability to track occluded targets. The results of experimental evaluation and comparison on the OTB100 and UAV123 data sets show that when the target has complex conditions such as scale changes, similar interference, occlusion, etc., the proposed algorithm performs well in mainstream related filtering algorithms. Through the quantitative and qualitative analysis of the experimental results, the effectiveness of the proposed algorithm is verified.</p> <p> </p>
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Chang-Jian Wang, Chang-Jian Wang, Yong Ding Chang-Jian Wang, and Ye Ji Yong Ding. "An Improved Kernel Correlation Filter Tracking Combined with Mobilenet SSD." 電腦學刊 33, no. 2 (April 2022): 069–81. http://dx.doi.org/10.53106/199115992022043302006.
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<p>This article mainly solves the problems that exist when using the Kernel Correlation Filter (KCF) for tracking in complex scenarios. To make the algorithm suitable for target tracking under complex conditions such as scale changes, similar interference, and occlusion, a MobileNet SSD (Single Shot Detection) target detection combined with an improved KCF target tracking algorithm is proposed. Firstly, the MobileNet SSD is used to locate the target in the initial frame, and the location is sent to KCF for training. Secondly, aiming at the problem of scale changes, a Binary-Tree scale search strategy is proposed. In this strategy, the scale value is searched in a tree shape according to the response size, which reduces the number of scale searches. Finally, the average peak correlation energy is used for occlusion determination, and the model update strategy is improved, thereby enhancing the algorithm’s ability to track occluded targets. The results of experimental evaluation and comparison on the OTB100 and UAV123 data sets show that when the target has complex conditions such as scale changes, similar interference, occlusion, etc., the proposed algorithm performs well in mainstream related filtering algorithms. Through the quantitative and qualitative analysis of the experimental results, the effectiveness of the proposed algorithm is verified.</p> <p> </p>
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Guo, Xifeng, Turdi Tohti, Mayire Ibrayim, and Askar Hamdulla. "Research on Anti-Occlusion Correlation Filtering Tracking Algorithm Based on Adaptive Scale." Information 13, no. 3 (March 4, 2022): 131. http://dx.doi.org/10.3390/info13030131.
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Target tracking has always been an important research direction in the field of computer vision. The target tracking method based on correlation filtering has become a research hotspot in the field of target tracking due to its efficiency and robustness. In recent years, a series of new developments have been made in this research. However, traditional correlation filtering algorithms cannot achieve real-time tracking in complex scenes such as illumination changes, target occlusion, motion deformation, and motion blur due to their single characteristics and insufficient background information. Therefore, a scale-adaptive anti-occlusion correlation filtering tracking algorithm is proposed. First, solve the single feature problem of traditional correlation filters through feature fusion. Secondly, the scale pyramid is introduced to solve the problem of tracking failure caused by scale changes. In this paper, two independent filters are trained, namely the position filter and the scale filter, to locate and scale the target, respectively. Finally, an occlusion judgment strategy is proposed to improve the robustness of the algorithm in view of the tracking drift problem caused by the occlusion of the target. In addition, the problem of insufficient background information in traditional correlation filtering algorithms is improved by adding context-aware background information. The experimental results show that the improved algorithm has a significant improvement in success rate and accuracy compared when with the traditional kernel correlation filter tracking algorithm. When the target has large scale changes or there is occlusion, the improved algorithm can still keep stable tracking.
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Wang, Zi-Hao, Wen-Jie Chen, and Kai-Yu Qin. "Dynamic Target Tracking and Ingressing of a Small UAV Using Monocular Sensor Based on the Geometric Constraints." Electronics 10, no. 16 (August 11, 2021): 1931. http://dx.doi.org/10.3390/electronics10161931.
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In many applications of airborne visual techniques for unmanned aerial vehicles (UAVs), lightweight sensors and efficient visual positioning and tracking algorithms are essential in a GNSS-denied environment. Meanwhile, many tasks require the ability of recognition, localization, avoiding, or flying pass through these dynamic obstacles. In this paper, for a small UAV equipped with a lightweight monocular sensor, a single-frame parallel-features positioning method (SPPM) is proposed and verified for a real-time dynamic target tracking and ingressing problem. The solution is featured with systematic modeling of the geometric characteristics of moving targets, and the introduction of numeric iteration algorithms to estimate the geometric center of moving targets. The geometric constraint relationships of the target feature points are modeled as non-linear equations for scale estimation. Experiments show that the root mean square error percentage of static target tracking is less than 1.03% and the root mean square error of dynamic target tracking is less than 7.92 cm. Comprehensive indoor flight experiments are conducted to show the real-time convergence of the algorithm, the effectiveness of the solution in locating and tracking a moving target, and the excellent robustness to measurement noises.
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Liu, Pu, Chun Ping Wang, and Qiang Fu. "An Anti- Occlusion Tracking Algorithm Based on MCD Correlation Matching." Advanced Materials Research 718-720 (July 2013): 2005–10. http://dx.doi.org/10.4028/www.scientific.net/amr.718-720.2005.
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In order to improve the stability of target tracking under occlusion conditions，on the basis of researching some target tracking algorithms, this paper presents an algorithm based on MCD correlation matching, which combines multi sub-templates matching and target movement prediction. Besides, for occlusion characteristics, corresponding template matching criterions and updating methods are put forward. Experimental results show that, comparing with the single template method which updating frame by frame, the proposed algorithm has a certain anti-occlusion ability with better stability and continuity of target tracking under occlusion conditions.
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Zhang, An Qing, Jing Shun Yao, and Hong Quan Chen. "A Parallel PDA Algorithm for Closely-Spaced Targets Tracking in Clutter." Applied Mechanics and Materials 380-384 (August 2013): 1035–38. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.1035.
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To reduce the computational complexity of JPDA and resolve closely-spaced targets efficiently, decompose JPDA into parallel PDA algorithms without regarding the correlation of targets. For a target, measurements are all clutter except the ones originated from itself. The PDA algorithm to track every single target with fixed covariance of states. When targets got closed within thick clutter, try to avoid coalescence and reduce complexity of association, maintaining the independence of targets through neglecting the assignment association model of measurement-to-track which used to happen once targets gates got overlapped. Simulations give arguments that parallel PDA has a better performance than JPDA in tracking closely-spaced targets.
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Mitra, Dipayan, Aranee Balachandran, and Ratnasingham Tharmarasa. "Ground Target Tracking Using an Airborne Angle-Only Sensor with Terrain Uncertainty and Sensor Biases." Sensors 22, no. 2 (January 10, 2022): 509. http://dx.doi.org/10.3390/s22020509.
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Airborne angle-only sensors can be used to track stationary or mobile ground targets. In order to make the problem observable in 3-dimensions (3-D), the height of the target (i.e., the height of the terrain) from the sea-level is needed to be known. In most of the existing works, the terrain height is assumed to be known accurately. However, the terrain height is usually obtained from Digital Terrain Elevation Data (DTED), which has different resolution levels. Ignoring the terrain height uncertainty in a tracking algorithm will lead to a bias in the estimated states. In addition to the terrain uncertainty, another common source of uncertainty in angle-only sensors is the sensor biases. Both these uncertainties must be handled properly to obtain better tracking accuracy. In this paper, we propose algorithms to estimate the sensor biases with the target(s) of opportunity and algorithms to track targets with terrain and sensor bias uncertainties. Sensor bias uncertainties can be reduced by estimating the biases using the measurements from the target(s) of opportunity with known horizontal positions. This step can be an optional step in an angle-only tracking problem. In this work, we have proposed algorithms to pick optimal targets of opportunity to obtain better bias estimation and algorithms to estimate the biases with the selected target(s) of opportunity. Finally, we provide a filtering framework to track the targets with terrain and bias uncertainties. The Posterior Cramer–Rao Lower Bound (PCRLB), which provides the lower bound on achievable estimation error, is derived for the single target filtering with an angle-only sensor with terrain uncertainty and measurement biases. The effectiveness of the proposed algorithms is verified by Monte Carlo simulations. The simulation results show that sensor biases can be estimated accurately using the target(s) of opportunity and the tracking accuracies of the targets can be improved significantly using the proposed algorithms when the terrain and bias uncertainties are present.
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Xue, Renzheng, Ming Liu, and Xiaokun Yu. "Visual Sequence Algorithm for Moving Object Tracking and Detection in Images." Contrast Media & Molecular Imaging 2021 (December 27, 2021): 1–7. http://dx.doi.org/10.1155/2021/3666622.
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Objective. The effects of different algorithms on detecting and tracking moving objects in images based on computer vision technology are studied, and the best algorithm scheme is confirmed. Methods. An automatic moving target detection and tracking algorithm based on the improved frame difference method and mean-shift was proposed to test whether the improved algorithm has improved the detection and tracking effect of moving targets. The algorithm improves the traditional three-frame difference method and introduces a single Gaussian background model to participate in target detection. The improved frame difference method is used to detect the target, and the position window and center of the target are determined. Combined with the mean-shift algorithm, it is determined whether the template needs to be updated according to whether it exceeds the set threshold so that the algorithm can automatically track the moving target. Results. The position and size of the search window change as the target location and size change. The Bhattacharyya similarity measure ρ (y) exceeds the threshold r, and the target detection algorithm is successfully restarted. Conclusion. The algorithm for automatic detection and tracking of moving objects based on the improved frame difference method and mean-shift is fast and has high accuracy.
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Wang, Ai Xia, Peng Wu, Jing Jiao Li, and Ai Yun Yan. "A Real Time Tracking Algorithm Basing on Mixed Difference Algorithm." Advanced Materials Research 108-111 (May 2010): 291–96. http://dx.doi.org/10.4028/www.scientific.net/amr.108-111.291.
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This paper presented a high real time target tracking algorithm – mixed difference tracking algorithm MDT. In the proposed algorithm, frame difference and background difference algorithms are combined to get the location of the target. With background difference algorithm the shape of the target can be extracted. Due to the affection of the dynamic background, single background difference algorithm can not get the location of the moving target. To solve this issue the frame difference algorithm is used to estimate the location, and then combine the results of the background difference algorithm and the frame difference algorithm the location and the size of the target can be extracted. And then filtering algorithm is used to remove noise and isolated points. In the experiment it can be seen that the proposed algorithm can tracking object precisely in real time.
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Yao, Ji, and Deepa Singh. "Motion Tracking and Testing Based on Improved Surendra Algorithm." Open Electrical & Electronic Engineering Journal 8, no. 1 (December 31, 2014): 620–24. http://dx.doi.org/10.2174/1874129001408010620.
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Recently, due to the gradual mature of the development of computer vision, video-based monitoring and control system has become a classic practice in the field of computer vision. Traffic detection and tracking technology in intelligent video surveillance system is one of the branches of computer vision, which has gradually become a hot and new research field. Through analysis and summary of the existing detection and tracking technology, this study draws a set of target detection and tracking program at the perspective of taking photos with a single fixed camera on the road. The target in the program is the vehicle on the road. The key point of the program is to detect the target, and another is tracking. The main purpose of this study is to detect and track the moving vehicles on the road in the condition of a single fixed camera. This detection program uses the improved surendra algorithm, which is a more advanced algorithm in the algorithms of moving target detection. In all the algorithms, such as background subtraction method and the adjacent frame difference method, the improved surendra algorithm is more excellent than them. The algorithm is based on the mixed Gaussian model method and the improved adjacent frame difference. Experiment shows that the algorithm is able to track and detect the target vehicle accurately indeed. And the complexity, real-time and robustness of the algorithm are very consistent with the system design requirements of the study, so the adoption of the algorithm and the implementation of the detection system design of this study can track and detect the target vehicle well.
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Li, Liping. "Migration Path Tracking Algorithm of Egret Birds Based on Intelligent Remote Sensing Monitoring." Wireless Communications and Mobile Computing 2022 (August 30, 2022): 1–10. http://dx.doi.org/10.1155/2022/4320297.
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There are billions of migratory birds migrating between breeding grounds and wintering grounds in the world every year. Egret is no exception. During the migration period, they need to travel for a long time and long distances. At present, the protection strategies of egrets and the dynamic mechanism of migration routes are concerned by experts in related fields. Researchers generally explore the above problems by tracking the migration paths of egrets. Most of the existing path tracking algorithms use some classical tracking algorithms, but the tracking accuracy of the classical algorithms is low. Therefore, this paper has applied the intelligent remote sensing monitoring technology to the improvement of the path tracking algorithm, used artificial intelligence technology to remove the redundant information of the image, and combined the Kalman filter with the single-target long-term algorithm to improve the tracking algorithm. 100 samples were simulated, and the experimental results showed that the tracking accuracy of the egret bird migration path tracking algorithm based on intelligent remote sensing monitoring was improved by 8.37% compared with the algorithm before improvement, which has better utilization value.
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Sheng, Xueli, Yang Chen, Longxiang Guo, Jingwei Yin, and Xiao Han. "Multitarget Tracking Algorithm Using Multiple GMPHD Filter Data Fusion for Sonar Networks." Sensors 18, no. 10 (September 21, 2018): 3193. http://dx.doi.org/10.3390/s18103193.
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Multitarget tracking algorithms based on sonar usually run into detection uncertainty, complex channel and more clutters, which cause lower detection probability, single sonar sensors failing to measure when the target is in an acoustic shadow zone, and computational bottlenecks. This paper proposes a novel tracking algorithm based on multisensor data fusion to solve the above problems. Firstly, under more clutters and lower detection probability condition, a Gaussian Mixture Probability Hypothesis Density (GMPHD) filter with computational advantages was used to get local estimations. Secondly, this paper provided a maximum-detection capability multitarget track fusion algorithm to deal with the problems caused by low detection probability and the target being in acoustic shadow zones. Lastly, a novel feedback algorithm was proposed to improve the GMPHD filter tracking performance, which fed the global estimations as a random finite set (RFS). In the end, the statistical characteristics of OSPA were used as evaluation criteria in Monte Carlo simulations, which showed this algorithm’s performance against those sonar tracking problems. When the detection probability is 0.7, compared with the GMPHD filter, the OSPA mean of two sensor and three sensor fusion was decrease almost by 40% and 55%, respectively. Moreover, this algorithm successfully tracks targets in acoustic shadow zones.
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Li, Yingchao, Lianji Ma, Shuai Yang, Qiang Fu, Hongyu Sun, and Chao Wang. "Infrared Image-Enhancement Algorithm for Weak Targets in Complex Backgrounds." Sensors 23, no. 13 (July 7, 2023): 6215. http://dx.doi.org/10.3390/s23136215.
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Infrared small-target enhancement in complex contexts is one of the key technologies for infrared search and tracking systems. The effect of enhancement directly determines the reliability of the monitoring equipment. To address the problem of the low signal-to-noise ratio of small infrared moving targets in complex backgrounds and the poor effect of traditional enhancement algorithms, an accurate enhancement method for small infrared moving targets based on two-channel information is proposed. For a single frame, a modified curvature filter is used in the A channel to weaken the background while an improved PM model is used to enhance the target, and a modified band-pass filter is used in the B channel for coarse enhancement followed by a local contrast algorithm for fine enhancement, based on which a weighted superposition algorithm is used to extract a single-frame candidate target. The results of the experimental data analysis prove that the method has a good enhancement effect and robustness for small IR motion target enhancement in complex backgrounds, and it outperforms other advanced algorithms by about 43.7% in ROC.
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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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Tian, Weihong. "Research on Curriculum Design Method of Teaching Resource Library based on Deep Learning Technology." Highlights in Science, Engineering and Technology 35 (April 11, 2023): 157–61. http://dx.doi.org/10.54097/hset.v35i.7048.
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In order to improve the ideological understanding of computer majors and establish a correct world outlook, outlook on life and values, the ideological and political goals of computer majors are designed; (1) A new deep learning face recognition algorithm is proposed. Aiming at the limitations of many current methods of artificially designing image description features, this algorithm proposes an unsupervised big data-driven deep image feature learning and representation mechanism. (2) A new idea of single-target tracking algorithm DPL is proposed. Single target tracking is a hot research topic in the field of computer anxiety. Our proposed DPL algorithm is still very competitive with the best algorithms in the field of object tracking. Several key technology research and model design schemes for computer major courses are proposed, which enriches the relevant research content of deep learning in the field of computer vision, and has certain theoretical and practical significance.
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Liu, Caihong, Mayire Ibrayim, and Askar Hamdulla. "Multi-Feature Single Target Robust Tracking Fused with Particle Filter." Sensors 22, no. 5 (February 27, 2022): 1879. http://dx.doi.org/10.3390/s22051879.
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Aiming at the problems of target model drift or loss of target tracking caused by serious deformation, occlusion, fast motion, and out of view of the target in long-term moving target tracking in complex scenes, this paper presents a robust multi-feature single-target tracking algorithm based on a particle filter. The algorithm is based on the correlation filtering framework. First, to extract more accurate target appearance features, in addition to the manual features histogram of oriented gradient features and color histogram features, the depth features from the conv3–4, conv4–4 and conv5–4 convolutional layer outputs in VGGNet-19 are also fused. Secondly, this paper designs a re-detection module of a fusion particle filter for the problem of how to return to accurate tracking after the target tracking fails, so that the algorithm in this paper can maintain high robustness during long-term tracking. Finally, in the adaptive model update stage, the adaptive learning rate update and adaptive filter update are performed to improve the accuracy of target tracking. Extensive experiments are conducted on dataset OTB-2015, dataset OTB-2013, and dataset UAV123. The experimental results show that the proposed multi-feature single-target robust tracking algorithm with fused particle filtering can effectively solve the long-time target tracking problem in complex scenes, while showing more stable and accurate tracking performance.
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Liu, Tingzhuang, Yi Zhu, Kefei Wu, and Fei Yuan. "Underwater Accompanying Robot Based on SSDLite Gesture Recognition." Applied Sciences 12, no. 18 (September 11, 2022): 9131. http://dx.doi.org/10.3390/app12189131.
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Underwater robots are often used in marine exploration and development to assist divers in underwater tasks. However, the underwater robots on the market have some problems, such as only a single function of object detection or tracking, the use of traditional algorithms with low accuracy and robustness, and the lack of effective interaction with divers. To this end, we designed a type of gesture recognition based on interaction, using person tracking as an auxiliary means for an underwater accompanying robot (UAR). We train and test the SSDLite detection algorithm using the self-labeled underwater datasets, and combine the kernelized correlation filters (KCF) tracking algorithm with the “Active Control” target tracking rule to continuously track the underwater human body. Our experiments show that the use of underwater datasets and target tracking can effectively improve gesture recognition accuracy by 40–105%. In the outfield experiment, the performance of the algorithm was good. It achieved target tracking and gesture recognition at 29.4 FPS on Jetson Xavier NX, and the UAR made corresponding actions according to the diver gesture command.
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Fu, Yu, and Xiang Long. "Pedestrian Tracking Based on Improved Particle Filter under Complex Background." Advanced Materials Research 756-759 (September 2013): 4103–9. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.4103.
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When tracking a moving human target,the traditional particle filter algorithm based on color characteristic can't get accurate results in situations like complicated background or frequent brightness change.Due to the problem, this paper put forward a particle filter algorithm based on combination of color characteristics and shape features of the target.Firstly, fusing the above-mentioned two features into the particle filter frame to calculate the particle weights and achieve the human tracking goal through image sequences . The experimental results show that the algorithm can improve the traditional tracking algorithms based on single color feature limitations.And greatly improves the accuracy and effectiveness of the human tracking under complex background
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Tan, Li, Xiaokai Huang, Xinyue Lv, Xujie Jiang, and He Liu. "Strong Interference UAV Motion Target Tracking Based on Target Consistency Algorithm." Electronics 12, no. 8 (April 8, 2023): 1773. http://dx.doi.org/10.3390/electronics12081773.
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In recent years, unmanned aerial vehicle (UAV) image target tracking technology, which obtains motion parameters of moving targets and achieves a behavioral understanding of moving targets by identifying, detecting and tracking moving targets in UAV images, has been widely used in urban safety fields such as accident rescue, traffic monitoring and personnel detection. Due to the problems of complex backgrounds, small scale and a high density of targets, as well as mutual occlusion among targets in UAV images, this leads to inaccurate results of single object tracking (SOT). To solve the problem of tracking target loss caused by inaccurate tracking results, this paper proposes a strong interference motion target tracking method based on the target consistency algorithm for SOT based on an interframe fusion and trajectory confidence mechanism, fusing previous frames for the tracking trajectory correction of current frames, learning again from previous frames to update the model and adjusting the tracking trajectory according to the tracking duration. The experimental results can show that the accuracy of the proposed method in this paper is improved by 6.3% and the accuracy is improved by 2.6% compared with the benchmark method, which is more suitable for applications in the case of background clutter, camera motion and viewpoint change.
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Huang, Yuan, Yifang Shi, and Taek Song. "An Efficient Multi-Path Multitarget Tracking Algorithm for Over-The-Horizon Radar." Sensors 19, no. 6 (March 20, 2019): 1384. http://dx.doi.org/10.3390/s19061384.
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In target tracking environments using over-the-horizon radar (OTHR), one target may generate multiple detections through different signal propagation paths. Trackers need to jointly handle the uncertainties stemming from both measurement origin and measurement path. Traditional multitarget tracking algorithms suffer from high computational loads in such environments since they need to enumerate all possible joint measurement-to-track assignments considering the measurements paths unless they employ some approximations regarding the measurements and their corresponding paths. In this paper, we propose a novel algorithm, named multi-path linear multitarget integrated probabilistic data association (MP-LM-IPDA), to efficiently track multitarget in multiple detection environments. Instead of generating all possible joint assignments, MP-LM-IPDA calculates the modulated clutter measurement density for each measurement cell of each track. The modulated clutter measurement density considers the possibility that the measurement cells originate from the clutter as well as from other potential targets. By incorporating the modulated clutter measurement density, the single target tracking structure can be applied for multitarget tracking, which significantly reduces the computational load. The simulation results demonstrate the effectiveness and efficiency of the proposed algorithm.
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Wu, Wei Hua, Jing Jiang, Xun Feng, and Chong Yang Liu. "Tracking a Maneuvering Target in Clutter by Asynchronous and Heterogeneous Sensors on Single Airborne Platform." Applied Mechanics and Materials 701-702 (December 2014): 154–59. http://dx.doi.org/10.4028/www.scientific.net/amm.701-702.154.
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A new algorithm is proposed for tracking a maneuvering target in clutter using heterogeneous sensors, such as active and passive sensors, which are carried on single moving airborne platform and report their observations asynchronously. The algorithm combining interacting multiple model (IMM) with probabilistic data association filter (PDAF) is sequentially implemented to cope with asynchronous reports. In addition, in order to close to practice, the algorithm is based on Earth-centered Earth-fixed (ECEF) coordinate system while it considers the effect of the platform’s attitude. So the algorithm extends previous algorithms from synchronous case to asynchronous case, from fixed station to moving airborne platform, and from local Cartesian coordinates to general ECEF coordinates. The simulation results show that the proposed algorithm has a broader and more practical scope while being slightly worse than existing algorithm which only be applied under synchronous case.
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Meng, Fanming. "Tennis Video Target Tracking Based on Mobile Network Communication and Machine Learning Algorithm." International Transactions on Electrical Energy Systems 2022 (September 10, 2022): 1–11. http://dx.doi.org/10.1155/2022/7447121.
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The application of information technology has realized the transformation of people’s production and lifestyle and also promoted the development of the sports industry. At present, the application of informatization in the field of sports is becoming more and more powerful. By using the advanced methods and technologies of its information display, this paper aimed to realize the research on the tracking of tennis sports video objects in the mobile network environment. It is helpful to analyze and solve the objectivity problems such as most of the loopholes in the single research and traditional methods of tennis video target tracking research. By drawing on the principles and rules of machine learning algorithms, the tennis video target tracking research is carried out, and the informatization and dataization of tennis are realized. In the experiment of the target tracking algorithm, 12 tracking videos have higher tracking accuracy than other parameters. The overall tracking accuracy of the video sequence under grayscale feature was 0.694; the tracking accuracy was the highest, but the tracking speed was the lowest compared to other parameters in the experiment. Therefore, it is very important to study the target tracking of tennis sports in the tennis field.
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YU, YUANLONG, GEORGE K. I. MANN, and RAYMOND G. GOSINE. "A SINGLE-OBJECT TRACKING METHOD FOR ROBOTS USING OBJECT-BASED VISUAL ATTENTION." International Journal of Humanoid Robotics 09, no. 04 (December 2012): 1250030. http://dx.doi.org/10.1142/s0219843612500302.
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It is a quite challenging problem for robots to track the target in complex environment due to appearance changes of the target and background, large variation of motion, partial and full occlusion, motion of the camera and so on. However, humans are capable to cope with these difficulties by using their cognitive capability, mainly including the visual attention and learning mechanisms. This paper therefore presents a single-object tracking method for robots based on the object-based attention mechanism. This tracking method consists of four modules: pre-attentive segmentation, top-down attentional selection, post-attentive processing and online learning of the target model. The pre-attentive segmentation module first divides the scene into uniform proto-objects. Then the top-down attention module selects one proto-object over the predicted region by using a discriminative feature of the target. The post-attentive processing module then validates the attended proto-object. If it is confirmed to be the target, it is used to obtain the complete target region. Otherwise, the recovery mechanism is automatically triggered to globally search for the target. Given the complete target region, the online learning algorithm autonomously updates the target model, which consists of appearance and saliency components. The saliency component is used to automatically select a discriminative feature for top-down attention, while the appearance component is used for bias estimation in the top-down attention module and validation in the post-attentive processing module. Experiments have shown that this proposed method outperforms other algorithms without using attention for tracking a single target in cluttered and dynamically changing environment.
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Chang, Hsien-Tsung, Shu-Wei Liu, and Nilamadhab Mishra. "A tracking and summarization system for online Chinese news topics." Aslib Journal of Information Management 67, no. 6 (November 16, 2015): 687–99. http://dx.doi.org/10.1108/ajim-10-2014-0147.
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Purpose – The purpose of this paper is to design and implement new tracking and summarization algorithms for Chinese news content. Based on the proposed methods and algorithms, the authors extract the important sentences that are contained in topic stories and list those sentences according to timestamp order to ensure ease of understanding and to visualize multiple news stories on a single screen. Design/methodology/approach – This paper encompasses an investigational approach that implements a new Dynamic Centroid Summarization algorithm in addition to a Term Frequency (TF)-Density algorithm to empirically compute three target parameters, i.e., recall, precision, and F-measure. Findings – The proposed TF-Density algorithm is implemented and compared with the well-known algorithms Term Frequency-Inverse Word Frequency (TF-IWF) and Term Frequency-Inverse Document Frequency (TF-IDF). Three test data sets are configured from Chinese news web sites for use during the investigation, and two important findings are obtained that help the authors provide more precision and efficiency when recognizing the important words in the text. First, the authors evaluate three topic tracking algorithms, i.e., TF-Density, TF-IDF, and TF-IWF, with the said target parameters and find that the recall, precision, and F-measure of the proposed TF-Density algorithm is better than those of the TF-IWF and TF-IDF algorithms. In the context of the second finding, the authors implement a blind test approach to obtain the results of topic summarizations and find that the proposed Dynamic Centroid Summarization process can more accurately select topic sentences than the LexRank process. Research limitations/implications – The results show that the tracking and summarization algorithms for news topics can provide more precise and convenient results for users tracking the news. The analysis and implications are limited to Chinese news content from Chinese news web sites such as Apple Library, UDN, and well-known portals like Yahoo and Google. Originality/value – The research provides an empirical analysis of Chinese news content through the proposed TF-Density and Dynamic Centroid Summarization algorithms. It focusses on improving the means of summarizing a set of news stories to appear for browsing on a single screen and carries implications for innovative word measurements in practice.
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Xu, Zheng, Haibo Luo, Bin Hui, and Zheng Chang. "Siamese Tracking from Single Point Initialization." Sensors 19, no. 3 (January 26, 2019): 514. http://dx.doi.org/10.3390/s19030514.
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Recently, we have been concerned with locating and tracking vehicles in aerial videos. Vehicles in aerial videos usually have small sizes due to use of cameras from a remote distance. However, most of the current methods use a fixed bounding box region as the input of tracking. For the purpose of target locating and tracking in our system, detecting the contour of the target is utilized and can help with improving the accuracy of target tracking, because a shape-adaptive template segmented by object contour contains the most useful information and the least background for object tracking. In this paper, we propose a new start-up of tracking by clicking on the target, and implement the whole tracking process by modifying and combining a contour detection network and a fully convolutional Siamese tracking network. The experimental results show that our algorithm has significantly improved tracking accuracy compared to the state-of-the-art regarding vehicle images in both OTB100 and DARPA datasets. We propose utilizing our method in real time tracking and guidance systems.
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Hu, Shuo, Yanan Ge, Jianglong Han, and Xuguang Zhang. "Object Tracking Algorithm Based on Dual Color Feature Fusion with Dimension Reduction." Sensors 19, no. 1 (December 25, 2018): 73. http://dx.doi.org/10.3390/s19010073.
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Aiming at the problem of poor robustness and the low effectiveness of target tracking in complex scenes by using single color features, an object-tracking algorithm based on dual color feature fusion via dimension reduction is proposed, according to the Correlation Filter (CF)-based tracking framework. First, Color Name (CN) feature and Color Histogram (CH) feature extraction are respectively performed on the input image, and then the template and the candidate region are correlated by the CF-based methods, and the CH response and CN response of the target region are obtained, respectively. A self-adaptive feature fusion strategy is proposed to linearly fuse the CH response and the CN response to obtain a dual color feature response with global color distribution information and main color information. Finally, the position of the target is estimated, based on the fused response map, with the maximum of the fused response map corresponding to the estimated target position. The proposed method is based on fusion in the framework of the Staple algorithm, and dimension reduction by Principal Component Analysis (PCA) on the scale; the complexity of the algorithm is reduced, and the tracking performance is further improved. Experimental results on quantitative and qualitative evaluations on challenging benchmark sequences show that the proposed algorithm has better tracking accuracy and robustness than other state-of-the-art tracking algorithms in complex scenarios.
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Zhang, Lieping, Jinghua Nie, Shenglan Zhang, Yanlin Yu, Yong Liang, and Zuqiong Zhang. "Research on the Particle Filter Single-Station Target Tracking Algorithm Based on Particle Number Optimization." Journal of Electrical and Computer Engineering 2021 (September 4, 2021): 1–8. http://dx.doi.org/10.1155/2021/2838971.
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Given that the tracking accuracy and real-time performance of the particle filter (PF) target tracking algorithm are greatly affected by the number of sampled particles, a PF target tracking algorithm based on particle number optimization under the single-station environment was proposed in this study. First, a single-station target tracking model was established, and the corresponding PF algorithm was designed. Next, a tracking simulation experiment was carried out on the PF target tracking algorithm under different numbers of particles with the root mean square error (RMSE) and filtering time as the evaluation indexes. On this basis, the optimal number of particles, which could meet the accuracy and real-time performance requirements, was determined and taken as the number of particles of the proposed algorithm. The MATLAB simulation results revealed that compared with the unscented Kalman filter (UKF), the single-station PF target tracking algorithm based on particle number optimization not only was of high tracking accuracy but also could meet the real-time performance requirement.
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Yao, Guang Yu, and Lu Song. "Design of the Target Tracking Process Based on DM648." Applied Mechanics and Materials 599-601 (August 2014): 904–7. http://dx.doi.org/10.4028/www.scientific.net/amm.599-601.904.
Full textAbstract:
Compared with the traditional vehicle detector, the vehicle detection and tracking based on video image processing and the technique of visual target has fast processing speed, and convenient installation and maintenance, and low cost, wide range of monitoring, can obtain more kinds of traffic parameters, and many other advantages, has become more and more widely used in intelligent transportation system (ITS) in recent years. This paper introduces a method for real-time detection, target tracking in traffic image sequences from a fixed single camera. The System adopts TMS320DM648 as the core processor to implement the real-time target tracking algorithms, mainly complete the effective information real-time display of the software and hardware design of target tracking system, application flexibility, small volume, stable and reliable, it is very practical in practice.
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Liu, Jiaqi, Zhen Wang, Di Cheng, Weidong Chen, and Chang Chen. "Marine Extended Target Tracking for Scanning Radar Data Using Correlation Filter and Bayes Filter Jointly." Remote Sensing 14, no. 23 (November 23, 2022): 5937. http://dx.doi.org/10.3390/rs14235937.
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As the radar resolution improves, the extended structure of the targets in radar echoes can make a significant contribution to improving tracking performance, hence specific trackers need to be designed for these targets. However, traditional radar target tracking methods are mainly based on the accumulation of the target’s motion information, and the target’s appearance information is ignored. In this paper, a novel tracking algorithm that exploits both the appearance and motion information of a target is proposed to track a single extended target in maritime surveillance scenarios by incorporating the Bayesian motion state filter and the correlation appearance filter. The proposed algorithm consists of three modules. Firstly, a Bayesian module is utilized to accumulate the motion information of the target. Secondly, a correlation module is performed to capture the appearance features of the target. Finally, a fusion module is proposed to integrate the results of the former two modules according to the Maximum A Posteriori Criterion. In addition, a feedback structure is proposed to transfer the fusion results back to the former two modules to improve their stability. Besides, a scale adaptive strategy is presented to improve the tracker’s ability to cope with targets with varying shapes. In the end, the effectiveness of the proposed method is verified by measured radar data. The experimental results demonstrate that the proposed method achieves superior performance compared with other traditional algorithms, which simply focus on the target’s motion information. Moreover, this method is robust under complicated scenarios, such as clutter interference, target shape changing, and low signal-to-noise ratio (SNR).
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Li, Jia Qi, Chu Guang Li, and Xiao Lin Tian. "Target Recognition and Tracking Algorithm without Training Samples: Sand-Table Algorithm." Advanced Materials Research 1061-1062 (December 2014): 1177–85. http://dx.doi.org/10.4028/www.scientific.net/amr.1061-1062.1177.
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In this paper, Working on the design of a algorithm :sand-table algorithm.The algorithm could work well in recognizing and tracking an single moving target shot by camera or in a video .The algorithm works simple with low operation cost.May used in tracking different object of many kinds.The algorithm imitate the the process of falling sands to Greatly enhance the tracking ability and tracking accuracy.
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Zhou, Fansen, Yidi Wang, Wei Zheng, Zhao Li, and Xin Wen. "Fast Distributed Multiple-Model Nonlinearity Estimation for Tracking the Non-Cooperative Highly Maneuvering Target." Remote Sensing 14, no. 17 (August 28, 2022): 4239. http://dx.doi.org/10.3390/rs14174239.
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The newly developed near-space vehicle has the characteristics of high speed and strong maneuverability, being able to perform vertical skips and a wide range of lateral maneuvers. Tracking this kind of target with ground-based radars is difficult because of the limited detection range caused by the curvature of the Earth. Compared with ground-based radars, satellite tracking platforms equipped with Synthetic Aperture Radars (SARs) have a wide detection range, and can keep the targets in custody, making them a promising approach to tracking near-space vehicles continuously. However, this approach may not work well, due to the unknown maneuvers of the non-cooperative target, and the limited computing power of the satellites. To enhance tracking stability and accuracy, and to lower the computational burden, we have proposed a Fast Distributed Multiple-Model (FDMM) nonlinearity estimation algorithm for satellites, which adopts a novel distributed multiple-model fusion framework. This approach first requires each satellite to perform local filtering based on its own single model, and the corresponding fusion factor derived by the Wasserstein distance is solved for each local estimate; then, after diffusing the local estimates, each satellite performs multiple-model fusion on the received estimates, based on the minimum weighted Kullback–Leibler divergence; finally, each satellite updates its state estimation according to the consensus protocol. Two simulation experiments revealed that the proposed FDMM algorithm outperformed the other four tracking algorithms: the consensus-based distributed multiple-model UKF; the improved consensus-based distributed multiple-model STUKF; the consensus-based strong-tracking adaptive CKF; and the interactive multiple-model adaptive UKF; the FDMM algorithm had high tracking precision and low computational complexity, showing its effectiveness for satellites tracking the near-space target.
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Elgamoudi, Abulasad, Hamza Benzerrouk, G. Arul Elango, and René Landry. "A Survey for Recent Techniques and Algorithms of Geolocation and Target Tracking in Wireless and Satellite Systems." Applied Sciences 11, no. 13 (June 30, 2021): 6079. http://dx.doi.org/10.3390/app11136079.
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A single Radio-Frequency Interference (RFI) is a disturbance source of modern wireless systems depending on Global Navigation Satellite Systems (GNSS) and Satellite Communication (SatCom). In particular, significant applications such as aeronautics and satellite communication can be severely affected by intentional and unintentional interference, which are unmitigated. The matter requires finding a radical and effective solution to overcome this problem. The methods used for overcoming the RFI include interference detection, interference classification, interference geolocation, tracking and interference mitigation. RFI source geolocation and tracking methodology gained universal attention from numerous researchers, specialists, and scientists. In the last decade, various conventional techniques and algorithms have been adopted in geolocation and target tracking in civil and military operations. Previous conventional techniques did not address the challenges and demand for novel algorithms. Hence there is a necessity for focussing on the issues associated with this. This survey introduces a review of various conventional geolocation techniques, current orientations, and state-of-the-art techniques and highlights some approaches and algorithms employed in wireless and satellite systems for geolocation and target tracking that may be extremely beneficial. In addition, a comparison between different conventional geolocation techniques has been revealed, and the comparisons between various approaches and algorithms of geolocation and target tracking have been addressed, including H∞ and Kalman Filtering versions that have been implemented and investigated by authors.
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Zhong, Lehai, Jiao Li, Feifan Zhou, Xiaoan Bao, Weiyin Xing, Zhengyong Han, and Jinsheng Luo. "Integration Between Cascade Region-Based Convolutional Neural Network and Bi-Directional Feature Pyramid Network for Live Object Tracking and Detection." Traitement du Signal 38, no. 4 (August 31, 2021): 1253–57. http://dx.doi.org/10.18280/ts.380437.
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The current target tracking and detection algorithms often have mistakes and omissions when the target is occluded or small. To overcome the defects, this paper integrates bi-directional feature pyramid network (BiFPN) into cascade region-based convolutional neural network (R-CNN) for live object tracking and detection. Specifically, the BiFPN structure was utilized to connect between scales and fuse weighted features more efficiently, thereby enhancing the network’s feature extraction ability, and improving the detection effect on occluded and small targets. The proposed method, i.e., Cascade R-CNN fused with BiFPN, was compared with target detection algorithms like Cascade R-CNN and single shot detection (SSD) on a video frame dataset of wild animals. Our method achieved a mean average precision (mAP) of 91%, higher than that of SSD and Cascade R-CNN. Besides, it only took 0.42s for our method to detect each image, i.e., the real-time detection was realized. Experimental results prove that the proposed live object tracking and detection model, i.e., Cascade R-CNN fused with BiFPN, can adapt well to the complex detection environment, and achieve an excellent detection effect.
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Wang, Jie Gui. "New Method of Moving Targets Passive Tracking by Single Moving Observer Based on Measurement Data Fusion." Applied Mechanics and Materials 239-240 (December 2012): 942–45. http://dx.doi.org/10.4028/www.scientific.net/amm.239-240.942.
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Moving targets passive tracking by single moving observer is a difficult problem. A new location method based on measurement data fusion is proposed in this paper. Firstly, the adaptive passive tracking initiation algorithm is introduced. Secondly, a new data association algorithm is proposed, based on the data fusion of multiple measurements, the decision of synthetic data association is made. Finally, with the help of computer simulations, the proposed algorithms are proven to be correct and effective.
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Xiong, Jiu Liang, Zhuang Zhi Han, and Hong Xu. "Study on Intermittent Target Tracking in Fire-Control Radar Network." Advanced Materials Research 219-220 (March 2011): 187–90. http://dx.doi.org/10.4028/www.scientific.net/amr.219-220.187.
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Intermittent target tracking (ITT) method is proposed based on radar network theory to keep the tracking precision while resisting anti-radiation missile effectively. ITT theory is analyzed first. Then, three relevant evaluation indexes and an improved adaptive sampling algorithm are proposed. Under the same condition, this paper simulated three different target tracking methods, i.e., single radar in ITT, single radar in fixed sampling period tracking and tracking method proposed in the paper. Simulation results show the validity of the method proposed in the paper.
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Zhang, Jianming, You Wu, Xiaokang Jin, Feng Li, and Jin Wang. "A Fast Object Tracker Based on Integrated Multiple Features and Dynamic Learning Rate." Mathematical Problems in Engineering 2018 (December 24, 2018): 1–14. http://dx.doi.org/10.1155/2018/5986062.
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Object tracking is a vital topic in computer vision. Although tracking algorithms have gained great development in recent years, its robustness and accuracy still need to be improved. In this paper, to overcome single feature with poor representation ability in a complex image sequence, we put forward a multifeature integration framework, including the gray features, Histogram of Gradient (HOG), color-naming (CN), and Illumination Invariant Features (IIF), which effectively improve the robustness of object tracking. In addition, we propose a model updating strategy and introduce a skewness to measure the confidence degree of tracking result. Unlike previous tracking algorithms, we judge the relationship of skewness values between two adjacent frames to decide the updating of target appearance model to use a dynamic learning rate. This way makes our tracker further improve the robustness of tracking and effectively prevents the target drifting caused by occlusion and deformation. Extensive experiments on large-scale benchmark containing 50 image sequences show that our tracker is better than most existing excellent trackers in tracking performance and can run at average speed over 43 fps.
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Li, Jia Qi. "Efficient Moving Target Tracking Algorithm: Design and Implementation of the Sand-Table Tracking Algorithm." Applied Mechanics and Materials 475-476 (December 2013): 1032–39. http://dx.doi.org/10.4028/www.scientific.net/amm.475-476.1032.
Full textAbstract:
Working on the design of a new algorithm :sand_table algorithm.The algorithm could work well in recognizing and tracking an single moving target shot by camera or in a video .The algorithm works simple with low operation cost.May used in tracking different object of many kinds.The algorithm imitate the the process of falling sands to Greatly enhance the tracking ability and tracking accuracy.
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Ban, Li Ying, Yue Hua Han, and Yan Hai Wu. "Target Tracking Based on Improved Camshift and Kalman Filter." Advanced Materials Research 989-994 (July 2014): 3587–90. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.3587.
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A tracking algorithm based on improved Camshift and Kalman filter is proposed in this paper to deal with the problems in traditional Camshift algorithm, such as tracking failure under color interference or occlusion. Firstly, the proposed algorithm improves the single color target model and presents a novel target model, which fuses color and motion cues, to enhance the robustness and accuracy of target tracking. And in order to increase the tracking efficiency, the algorithm combines Kalman filter with the improved Camshift algorithm by using Kalman filter to predict the position of the tracking target under color noises and occlusion. The experiment results demonstrate that the proposed algorithm can track the target object accurately and has better robustness.
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Hu, Ji, and Sai-wai Wong. "Design of efficient single target real-time beam tracking system." Journal of Physics: Conference Series 2245, no. 1 (April 1, 2022): 012006. http://dx.doi.org/10.1088/1742-6596/2245/1/012006.
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Abstract This paper builds a system for real-time tracking of a single target Radio Frequency (RF) signal source, in which the transmitter uses a low-cost HackRF One software radio transceiver platform, and the receiving antenna array uses two monopole antennas to form a 1×2 receiving array. The receiver uses Xilinx 7-Series development board Zedboard and AD9361 RF transceiver to build a wireless receiving platform to process the received signal. In this RF beam tracking system, it needs to perform Direction of Arrival (DOA) estimation on the calibrated signal, the DOA algorithm plays a crucial role in the accuracy of beam pointing and system running time, the MUSIC algorithm takes some time to conduct spectrum peak search, so the Root-Music algorithm is used here to avoid the spectrum peak search problem. To enable the Root-MUSIC algorithm to be better executed on this two-element receiving platform, this work simplifies the entire calculation process, requiring only a small amount of calculation and running time to complete the DOA estimation step, which effectively save FPGA processing time and computing resources. It provides a guarantee for the real-time performance of the system.
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Lv, Dong Yue, Dong Yan Liu, Zhi Pei Huang, Neng Hai Yu, and Jian Kang Wu. "Video Sequences Foreground Enhancement Using Hidden Markov Model." Advanced Materials Research 989-994 (July 2014): 3872–76. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.3872.
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Foreground detection is an important part in video surveillance system. The detection results will significantly affect the performance of tracking, abnormal behavior analysis and other following procedures. Many algorithms have been proposed to improve the detection performance. However, these algorithms simply focus on one single frame, ignoring the relationship among the detection results of one target in successive frames. This paper presents a novel foreground enhancement algorithm using Hidden Markov Model (HMM). In a video sequence, one target in successive frames usually has similar shape, size, et al. With this property, the target can be modeled by HMM and enhanced using the result of its prior frame. The observation of HMM is obtained by ViBe. The enhancement result is then estimated by using Maximum A Posteriori (MAP). Experimental results show that compared with the state-of-art algorithm, the proposed method can enhance foreground detection effectively.
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Zhang, Yuanshi, Minghai Pan, and Qinghua Han. "Joint Sensor Selection and Power Allocation Algorithm for Multiple-Target Tracking of Unmanned Cluster Based on Fuzzy Logic Reasoning." Sensors 20, no. 5 (March 2, 2020): 1371. http://dx.doi.org/10.3390/s20051371.
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The unmanned aerial vehicle (UAV) cluster is gradually attracting more attention, which takes advantage over a traditional single manned platform. Because the size of the UAV platform limits the transmitting power of its own radar, how to reduce the transmitting power while meeting the detection accuracy is necessary. Aim at multiple-target tracking (MTT), a joint radar node selection and power allocation algorithm for radar networks is proposed. The algorithm first uses fuzzy logic reasoning (FLR) to obtain the priority of targets to radars, and designs a radar clustering algorithm based on the priority to form several subradar networks. The radar clustering algorithm simplifies the problem of multiple-radar tracking multiple-target into several problems of multiple-radar tracking a single target, which avoids complex calculations caused by multiple variables in the objective function of joint radar node selection and power allocation model. Considering the uncertainty of the target RCS in practice, the chance-constraint programming (CCP) is used to balance power resource and tracking accuracy. Through the joint radar node selection and power allocation algorithm, the radar networks can use less power resource to achieve a given tracking performance, which is more suitable for working on drone platforms. Finally, the simulation proves the effectiveness of the algorithm.
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Xiao, Song, Xian Si Tan, and Hong Wang. "Interacting Multiple Mode Tracking Algorithm Based on Modified Coordinate Turn Model." Applied Mechanics and Materials 610 (August 2014): 534–39. http://dx.doi.org/10.4028/www.scientific.net/amm.610.534.
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The continuing success of near space hypersonic aircraft flight test has become a real threat to China's space attack-defense system, In view of the problem that the single model cannot track such target effectively, an interacting multiple model (IMM) tracking algorithm based on modified cornering model (MCT) was proposed. First the characteristics of near space hypersonic target were analyzed, and then the target real-time angular velocity according to the target motion equation was estimated, finally the near space hypersonic target tracking through the IMM was carried out. The Monte Carlo simulation results show that the IMM tracking algorithm can effectively track near space hypersonic target, and the tracking accuracy and stability are superior to single model, it has certain practical significance.
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Truong, Xuan Tung. "DEEP LEARNING TECHNIQUE - BASED DRONE DETECTION AND TRACKING." Journal of Military Science and Technology, no. 73 (June 15, 2021): 10–19. http://dx.doi.org/10.54939/1859-1043.j.mst.73.2021.10-19.
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The usage of small drones/UAVs is becoming increasingly important in recent years. Consequently, there is a rising potential of small drones being misused for illegal activities such as terrorism, smuggling of drugs, etc. posing high-security risks. Hence, tracking and surveillance of drones are essential to prevent security breaches. This paper resolves the problem of detecting small drones in surveillance videos using deep learning algorithms. Single Shot Detector (SSD) object detection algorithm and MobileNet-v2 architecture as the backbone were used for our experiments. The pre-trained model was re-trained on custom drone synthetic dataset by using transfer learning’s fine-tune technique. The results of detecting drone in our experiments were around 90.8%. The combination of drone detection, Dlib correlation tracking algorithm and centroid tracking algorithm effectively detects and tracks the small drone in various complex environments as well as is able to handle multiple target appearances.