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Journal articles on the topic 'Multiobject'

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Author: Grafiati

Published: 10 March 2023

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1

Zhang, Liwei, Jiahong Lai, Zenghui Zhang, Zhen Deng, Bingwei He, and Yucheng He. "Multimodal Multiobject Tracking by Fusing Deep Appearance Features and Motion Information." Complexity 2020 (September 25, 2020): 1–10. http://dx.doi.org/10.1155/2020/8810340.

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Multiobject Tracking (MOT) is one of the most important abilities of autonomous driving systems. However, most of the existing MOT methods only use a single sensor, such as a camera, which has the problem of insufficient reliability. In this paper, we propose a novel Multiobject Tracking method by fusing deep appearance features and motion information of objects. In this method, the locations of objects are first determined based on a 2D object detector and a 3D object detector. We use the Nonmaximum Suppression (NMS) algorithm to combine the detection results of the two detectors to ensure the detection accuracy in complex scenes. After that, we use Convolutional Neural Network (CNN) to learn the deep appearance features of objects and employ Kalman Filter to obtain the motion information of objects. Finally, the MOT task is achieved by associating the motion information and deep appearance features. A successful match indicates that the object was tracked successfully. A set of experiments on the KITTI Tracking Benchmark shows that the proposed MOT method can effectively perform the MOT task. The Multiobject Tracking Accuracy (MOTA) is up to 76.40% and the Multiobject Tracking Precision (MOTP) is up to 83.50%.

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2

Alam, M. S., O. Perez, and M. A. Karim. "Preprocessed multiobject joint transform correlator." Applied Optics 32, no. 17 (June 10, 1993): 3102. http://dx.doi.org/10.1364/ao.32.003102.

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3

Chu, Fu-Jen, Ruinian Xu, and Patricio A. Vela. "Real-World Multiobject, Multigrasp Detection." IEEE Robotics and Automation Letters 3, no. 4 (October 2018): 3355–62. http://dx.doi.org/10.1109/lra.2018.2852777.

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4

Ledyard, John O., David Porter, and Antonio Rangel. "Experiments Testing Multiobject Allocation Mechanisms." Journal of Economics & Management Strategy 6, no. 3 (September 1, 1997): 639–75. http://dx.doi.org/10.1162/105864097567228.

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5

Ledyard, John O., David Porter, and Antonio Rangel. "Experiments Testing Multiobject Allocation Mechanisms." Journal of Economics Management Strategy 6, no. 3 (September 1997): 639–75. http://dx.doi.org/10.1111/j.1430-9134.1997.00639.x.

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6

Shen, Jianbing, Zhiyuan Liang, Jianhong Liu, Hanqiu Sun, Ling Shao, and Dacheng Tao. "Multiobject Tracking by Submodular Optimization." IEEE Transactions on Cybernetics 49, no. 6 (June 2019): 1990–2001. http://dx.doi.org/10.1109/tcyb.2018.2803217.

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7

Li, Hui, Yapeng Liu, Wenzhong Lin, Lingwei Xu, and Junyin Wang. "Data Association Methods via Video Signal Processing in Imperfect Tracking Scenarios: A Review and Evaluation." Mathematical Problems in Engineering 2020 (August 31, 2020): 1–26. http://dx.doi.org/10.1155/2020/7549816.

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In 5G scenarios, there are a large number of video signals that need to be processed. Multiobject tracking is one of the main directions in video signal processing. Data association is a very important link in tracking algorithms. Complexity and efficiency of association method have a direct impact on the performance of multiobject tracking. Breakthroughs have been made in data association methods based on deep learning, and the performance has been greatly improved compared with traditional methods. However, there is a lack of overviews about data association methods. Therefore, this article first analyzes characteristics and performance of three traditional data association methods and then focuses on data association methods based on deep learning, which is divided into different deep network structures: SOT methods, end-to-end methods, and Wasserstein metric methods. The performance of each tracking method is compared and analyzed. Finally, it summarizes the current common datasets and evaluation criteria for multiobject tracking and discusses challenges and development trends of data association technology and data association methods which ensure robust and real time need to be continuously improved.

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8

Wang, Po-Tong, Jia-Shing Sheu, and Jing-Han Lai. "Camera Handoff for Multicamera Multiobject Tracking." Sensors and Materials 34, no. 2 (February 14, 2022): 563. http://dx.doi.org/10.18494/sam3616.

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9

Fantacci, Claudio, Ba-Ngu Vo, Ba-Tuong Vo, Giorgio Battistelli, and Luigi Chisci. "Robust Fusion for Multisensor Multiobject Tracking." IEEE Signal Processing Letters 25, no. 5 (May 2018): 640–44. http://dx.doi.org/10.1109/lsp.2018.2811750.

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10

Gu, Qingyi, Takeshi Takaki, and Idaku Ishii. "Fast FPGA-Based Multiobject Feature Extraction." IEEE Transactions on Circuits and Systems for Video Technology 23, no. 1 (January 2013): 30–45. http://dx.doi.org/10.1109/tcsvt.2012.2202195.

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11

Gao, Lin, Giorgio Battistelli, and Luigi Chisci. "Multiobject Fusion With Minimum Information Loss." IEEE Signal Processing Letters 27 (2020): 201–5. http://dx.doi.org/10.1109/lsp.2019.2963817.

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12

Wada, Toshikazu, Masayuki Sato, and Takashi Matsuyama. "Multiobject behavior recognition by selective attention." Electronics and Communications in Japan (Part III: Fundamental Electronic Science) 84, no. 9 (2001): 56–66. http://dx.doi.org/10.1002/ecjc.1035.

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13

Herbst, T. M., E. Pitz, and C. M. Reuther. "An Infrared Multiobject Fiber–Fed Spectrograph for the Calar Alto Observatory." International Astronomical Union Colloquium 149 (1995): 221–22. http://dx.doi.org/10.1017/s0252921100023009.

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14

Frandsen, Søren. "STACC." Symposium - International Astronomical Union 162 (1994): 49–50. http://dx.doi.org/10.1017/s0074180900214502.

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At the 1992 GONG Meeting in Boulder (ASP Conf. Ser. Vol. 42) an attempt was done to form a group of people interested in multiobject, multisite observations with CCD cameras on moderate size telescopes (D ≈ 1m). The motivations is the need for uninterrupted, long time strings of photometry of certain types of variables. Observations of single objects by networks of photoelectric photometers have been practiced for some time. The extension to multiobject, differential CCD photometry not only multiplies the output, but also provide better signal/noise results. Many of the objects of interest (δ-Scuti's, β-Cephei's) are found close together in open clusters, which already serve as testbeds for stellar evolution theories. Nevertheless, suitable target fields are difficult to find.

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15

Bao, Ke, and Yourong Ding. "Multiobjects Association and Abnormal Behavior Detection for Massive Data Analysis in Multisensor Monitoring Network." Mathematical Problems in Engineering 2020 (November 3, 2020): 1–9. http://dx.doi.org/10.1155/2020/8858416.

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With the rapid increase in the number of large-scale distributed cameras and the rapid increase in the monitoring range of the camera network, how to accurately recognize and analyze abnormal behavior is still a challenging problem. In addition, the appearance of moving objects between different cameras without overlapping fields of view undergoes significant changes, making it difficult to obtain accurate association Therefore, multiobjects association and abnormal behavior detection for massive data analysis in multisensor monitoring network are proposed in this paper, which firstly uses belief propagation to associate multiple objects, extracts the object’s behavior trajectory characteristics, and then builds a long short-term memory classification network to realize automatic classification of abnormal behaviors. Multiobject association fully considers the timing correlation and object detection probability, as well as the statistical dependence of the measurement on the association matrix. The experimental results show that our proposed method can achieve a high classification accuracy and sensitivity, which meets the requirements of automatic classification of abnormal behavior in complex monitoring network. This further shows that this research has practical application value.

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16

Brіukhan, S. F., and N. V. Sirotenko. "PECULIARITIES OF PERFORMING MULTIOBJECT FORENSIC HANDWRITING EXAMINATIONS." Theory and Practice of Forensic Science and Criminalistics 18 (December 26, 2018): 295–300. http://dx.doi.org/10.32353/khrife.2018.33.

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The specifics of multiobject forensic handwriting examination is considered. Urgency of their performing and the importance for making decisions on the case are underlined. Attention is drawn to the solution of specific problems that require different approaches, depending on the nature of research objects. Multistage character of research on a multitude of handwriting objects is singled out and features of a separate stage, namely preliminary research are considered. Importance of this stage for the further research planning, selection of the corresponding separate techniques by expert, construction of expert versions is revealed. It is pointed out that methods of performing multiobject handwriting examination at the stage of preliminary research has features that distinguish it from other handwriting methods. Issues related to familiarization with the submitted materials, verification of comparability of samples and establishment of comparative material sufficiency are touched upon. Importance of resolving the issue regarding the need to send an application by a expert is noted. Also, the completion of the preliminary research stage by the formulation of general and private versions per se of the tasks assigned to expert is noted. Recently expert practice requires from experts not only to update methodological approaches in solving handwriting tasks when granting a large number of objects, but also to improve methods of performing relevant research. Above mentioned sequence of research stages can be one of the types of guarantee for full and comprehensive objective research on a large number of objects, an serve the basis for a correct expert decision. Consideration of the issues of features on the stage of preliminary research in carrying out a multiobject handwriting examination is important for handwriting experts, both beginners and with great experience.

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17

Lutz, Thomas E., Thomas Ingerson, and William G. Weller. "Progress toward a multiobject radial-velocity spectrometer." Astronomical Journal 94 (December 1987): 1686. http://dx.doi.org/10.1086/114603.

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18

Ren, Yayun, and Benlian Xu. "A Quantitative Analysis on Two RFS-Based Filtering Methods for Multicell Tracking." Mathematical Problems in Engineering 2014 (2014): 1–17. http://dx.doi.org/10.1155/2014/495765.

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Multiobject filters developed from the theory of random finite sets (RFS) have recently become well-known methods for solving multiobject tracking problem. In this paper, we present two RFS-based filtering methods, Gaussian mixture probability hypothesis density (GM-PHD) filter and multi-Bernoulli filter, to quantitatively analyze their performance on tracking multiple cells in a series of low-contrast image sequences. The GM-PHD filter, under linear Gaussian assumptions on the cell dynamics and birth process, applies the PHD recursion to propagate the posterior intensity in an analytic form, while the multi-Bernoulli filter estimates the multitarget posterior density through propagating the parameters of a multi-Bernoulli RFS that approximates the posterior density of multitarget RFS. Numerous performance comparisons between the two RFS-based methods are carried out on two real cell images sequences and demonstrate that both yield satisfactory results that are in good agreement with manual tracking method.

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19

Wang, Dengjiang, Chao Huang, Yajun Wang, Yongqiang Deng, and Hongqiang Li. "A 3D Multiobject Tracking Algorithm of Point Cloud Based on Deep Learning." Mathematical Problems in Engineering 2020 (December 10, 2020): 1–10. http://dx.doi.org/10.1155/2020/8895696.

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3D multiobject tracking (MOT) is an important part of road condition detection and hazard warning algorithm in roadside systems and autonomous driving systems. There is a tricky problem in 3D MOT that the identity of occluded object switches after it reappears. Given the good performance of the 2D MOT, this paper proposes a 3D MOT algorithm with deep learning based on the multiobject tracking algorithm. Firstly, a 3D object detector was used to obtain oriented 3D bounding boxes from point clouds. Secondly, a 3D Kalman filter was used for state estimation, and reidentification algorithm was used to match feature similarity. Finally, data association was conducted by combining Hungarian algorithm. Experiments show that the proposed method can still match the original trajectory after the occluded object reappears and run at a rate of 59 FPS, which has achieved advanced results in the existing 3D MOT system.

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20

Xu, Yue, Antonio A. Trani, and Hojong Baik. "Preliminary Assessment of Lower Landing Minima Capabilities in the Small Aircraft Transportation System Program." Transportation Research Record: Journal of the Transportation Research Board 1915, no. 1 (January 2005): 1–11. http://dx.doi.org/10.1177/0361198105191500101.

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A preliminary assessment is presented of the required lower landing minima (LLM) capabilities needed to support the Small Aircraft Transportation System (SATS) Program. The goal of this analysis is to understand the number of potentially challenged SATS airports and to identify methods to remove obstacles by using technology solutions. Four obstacle removal methods are considered to assess the challenges faced by the SATS Program in providing LLM capabilities to 3,416 U.S. airports. Two views of runway obstacle analysis are presented: a critical object analysis and a detailed multiobject analysis that includes terrain information. A comparison is made between decision altitudes (DAs) derived by approach lighting infrastructure and glide path angle thresholds and DA values considering other airport characteristics such as terrain. A detailed case study is presented to compare the single critical object analysis with the more detailed multiobject analysis, which was performed for Blacksburg Airport, in Virginia.

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21

Gong, Xuan, Zichun Le, Yukun Wu, and Hui Wang. "Real-Time Multiobject Tracking Based on Multiway Concurrency." Sensors 21, no. 3 (January 20, 2021): 685. http://dx.doi.org/10.3390/s21030685.

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This paper explored a pragmatic approach to research the real-time performance of a multiway concurrent multiobject tracking (MOT) system. At present, most research has focused on the tracking of single-image sequences, but in practical applications, multiway video streams need to be processed in parallel by MOT systems. There have been few studies on the real-time performance of multiway concurrent MOT systems. In this paper, we proposed a new MOT framework to solve multiway concurrency scenario based on a tracking-by-detection (TBD) model. The new framework mainly focuses on concurrency and real-time based on limited computing and storage resources, while considering the algorithm performance. For the former, three aspects were studied: (1) Expanded width and depth of tracking-by-detection model. In terms of width, the MOT system can support the process of multiway video sequence at the same time; in terms of depth, image collectors and bounding box collectors were introduced to support batch processing. (2) Considering the real-time performance and multiway concurrency ability, we proposed one kind of real-time MOT algorithm based on directly driven detection. (3) Optimization of system level—we also utilized the inference optimization features of NVIDIA TensorRT to accelerate the deep neural network (DNN) in the tracking algorithm. To trade off the performance of the algorithm, a negative sample (false detection sample) filter was designed to ensure tracking accuracy. Meanwhile, the factors that affect the system real-time performance and concurrency were studied. The experiment results showed that our method has a good performance in processing multiple concurrent real-time video streams.

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22

Liang Min, 梁敏, and 刘贵喜 Liu Guixi. "MultiObject Tracking Algorithm Based on Adaptive Mixed Filtering." Acta Optica Sinica 30, no. 9 (2010): 2554–61. http://dx.doi.org/10.3788/aos20103009.2554.

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23

Yeasin, M., E. Polat, and R. Sharma. "A Multiobject Tracking Framework for Interactive Multimedia Applications." IEEE Transactions on Multimedia 6, no. 3 (June 2004): 398–405. http://dx.doi.org/10.1109/tmm.2004.827514.

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24

Zamkotsian, Frederic. "Uniform tilt-angle micromirror array for multiobject spectroscopy." Journal of Micro/Nanolithography, MEMS, and MOEMS 7, no. 2 (April 1, 2008): 021014. http://dx.doi.org/10.1117/1.2920338.

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25

Leyssen, Mieke H. R., Sarah Linsen, Jonathan Sammartino, and Stephen E. Palmer. "Aesthetic Preference for Spatial Composition in Multiobject Pictures." i-Perception 3, no. 1 (January 2012): 25–49. http://dx.doi.org/10.1068/i0458aap.

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26

Feng, J. H., G. F. Chin, M. X. Wu, S. H. Yan, and Y. B. Yan. "Multiobject recognition in a multichannel joint-transform correlator." Optics Letters 20, no. 1 (January 1, 1995): 82. http://dx.doi.org/10.1364/ol.20.000082.

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27

Al-Hamadi, A. K., R. Niese, and B. Michaelis. "Multiobject tracking in video using a trisection paradigm." Pattern Recognition and Image Analysis 17, no. 4 (December 2007): 493–507. http://dx.doi.org/10.1134/s1054661807040086.

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28

Szentgyorgyi, Andrew. "Hectochelle: High resolution multiobject spectroscopy at the MMT." New Astronomy Reviews 50, no. 4-5 (June 2006): 326–28. http://dx.doi.org/10.1016/j.newar.2006.02.002.

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29

Hamilton, Donald, J. B. Oke, M. A. Carr, J. Cromer, F. H. Harris, J. Cohen, E. Emery, and L. Blakee. "A multiobject fiber spectrograph for The Hale Telescope." Publications of the Astronomical Society of the Pacific 105 (November 1993): 1308. http://dx.doi.org/10.1086/133312.

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30

BAŞKURT, Kemal Batuhan, and Refik SAMET. "Long-term multiobject tracking using alternative correlation filters." TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES 26, no. 5 (September 28, 2018): 2246–59. http://dx.doi.org/10.3906/elk-1709-245.

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31

Yang, X. H., L. Shafai, and A. Sebak. "Efficient technique for analysis of multiobject scattering problems." Electronics Letters 28, no. 15 (1992): 1442. http://dx.doi.org/10.1049/el:19920918.

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32

Joo, Seong-Wook, and Rama Chellappa. "A Multiple-Hypothesis Approach for Multiobject Visual Tracking." IEEE Transactions on Image Processing 16, no. 11 (November 2007): 2849–54. http://dx.doi.org/10.1109/tip.2007.906254.

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33

ZAJDEL, WOJCIECH, and BEN J. A. KRÖSE. "A SEQUENTIAL BAYESIAN ALGORITHM FOR SURVEILLANCE WITH NONOVERLAPPING CAMERAS." International Journal of Pattern Recognition and Artificial Intelligence 19, no. 08 (December 2005): 977–96. http://dx.doi.org/10.1142/s0218001405004423.

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Visual surveillance in wide areas (e.g. airports) relies on sparsely distributed cameras, that is, cameras that observe nonoverlapping scenes. In this setup, multiobject tracking requires reidentification of an object when it leaves one field of view, and later appears at some other. Although similar association problems are common for multiobject tracking scenarios, in the distributed case one has to cope with asynchronous observations and cannot assume smooth motion of the objects. In this paper, we propose a method for human indoor tracking. The method is based on a Dynamic Bayes Network (DBN) as a probabilistic model for the observations. The edges of the network define the correspondences between observations of the same object. Accordingly, we derive an approximate EM-like method for selecting the most likely structure of DBN and learning model parameters. The presented algorithm is tested on a collection of real-world observations gathered by a system of cameras in an office building.

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34

Cong, Lanmei, Xiaocong Li, and Ancai Zhang. "Multiobject Holographic Feedback Control of Differential Algebraic System with Application to Power System." Mathematical Problems in Engineering 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/415281.

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A multiobject holographic feedback (MOHF) control method for studying the nonlinear differential algebraic (NDA) system is proposed. In this method, the nonlinear control law is designed in a homeomorphous linear space by means of constructing the multiobject equations (MOEq) which is in accord with Brunovsky normal form. The objective functions of MOEq are considered to be the errors between the output functions and their references. The relative degree for algebraic system is defined that is key to connecting the nonlinear and the linear control laws. Pole assignment method is addressed for the stability domain of this MOHF control. Since there is no any approximation, the MOHF control is effective in governing the dynamic performance stably both to the small and major disturbance. The application in single machine infinite system (SMIS) shows that this approach is effective in the improvement of stable and transient stability for power system on the disturbance of active power or three-phase short circuit fault.

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35

HAGHVERDI, ESFANDIAR, and PHILIP SCOTT. "Towards a typed Geometry of Interaction." Mathematical Structures in Computer Science 20, no. 3 (April 21, 2010): 473–521. http://dx.doi.org/10.1017/s096012951000006x.

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Girard's Geometry of Interaction (GoI) develops a mathematical framework for modelling the dynamics of cut elimination. We introduce a typed version of GoI, called Multiobject GoI for both multiplicative linear logic (MLL) and multiplicative exponential linear logic (MELL) with units. We present a categorical setting that includes our previous (untyped) GoI models, as well as more general models based on monoidal *-categories. Our development of multiobject GoI depends on a new theory of partial traces and trace classes, which we believe is of independent interest, as well as an abstract notion of orthogonality (which is related to work of Hyland and Schalk). We develop Girard's original theory of types, data and algorithms in our setting, and show his execution formula to be an invariant of cut elimination (under some restrictions). We prove soundness theorems for the MGoI interpretation (for Multiplicative and Multiplicative Exponential Linear Logic) in partially traced *-categories with an orthogonality. Finally, we briefly discuss the relationship between our GoI interpretation and other categorical interpretations of GoI.

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36

Zhu, Hong Guo, Hai Xin, and Chang Wen Zheng. "A Multipath Planner for UAV Based on Pareto Optimization." Applied Mechanics and Materials 58-60 (June 2011): 2356–59. http://dx.doi.org/10.4028/www.scientific.net/amm.58-60.2356.

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A multipath planner for UAV based on Pareto optimization is proposed to overcome the disadvantage of planners existed. Multipath planning is modeled as a constrained multiobject optimization problem. A Pareto solution of multipath for UAV is generated by optimizing several object functions at the same time. The simulation results demonstrated the feasibility of the approach.

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37

Li, Jun. "Multiobject optimization of a centrifugal impeller using evolutionary algorithms." Chinese Journal of Mechanical Engineering (English Edition) 17, no. 03 (2004): 389. http://dx.doi.org/10.3901/cjme.2004.03.389.

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38

Bai, Yinghao, Xinchen Tao, Xin Chen, Jingyi Yu, and Chaofeng Ye. "Fusion Images of Versatile Array Sensors for Multiobject Detection." IEEE Transactions on Instrumentation and Measurement 70 (2021): 1–9. http://dx.doi.org/10.1109/tim.2021.3124842.

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39

Narykov, Alexey, Emmanuel Delande, and Daniel E. Clark. "A Formulation of the Adversarial Risk for Multiobject Filtering." IEEE Transactions on Aerospace and Electronic Systems 57, no. 4 (August 2021): 2082–92. http://dx.doi.org/10.1109/taes.2021.3098130.

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40

Szentgyorgyi, Andrew, Gabor Furesz, Peter Cheimets, Maureen Conroy, Roger Eng, Daniel Fabricant, Robert Fata, et al. "Hectochelle: A Multiobject Optical Echelle Spectrograph for the MMT." Publications of the Astronomical Society of the Pacific 123, no. 908 (October 2011): 1188–209. http://dx.doi.org/10.1086/662209.

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41

Yu, Francis T. S., Feng Cheng, Toshio Nagata, and Don A. Gregory. "Effects of fringe binarization of multiobject joint transform correlation." Applied Optics 28, no. 15 (August 1, 1989): 2988. http://dx.doi.org/10.1364/ao.28.002988.

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42

Waldis, Severin, Frederic Zamkotsian, Pierre-Andre Clerc, Wilfried Noell, Michael Zickar, and Nico de de Rooij. "Arrays of High Tilt-Angle Micromirrors for Multiobject Spectroscopy." IEEE Journal of Selected Topics in Quantum Electronics 13, no. 2 (2007): 168–76. http://dx.doi.org/10.1109/jstqe.2007.894181.

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43

Liang, Yitao, Deshan Zhang, Feng Wang, Yonggang Li, and Meng Zhang. "Method of Multiobject Detecting and Tracking Based on DM643." Mathematical Problems in Engineering 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/365480.

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The technology of moving objects detection has become an important research subject for its extensive application prospect. In this paper, it is presented that interframe difference algorithm and background difference algorithm are combined to update the background. The algorithm can deal with the flaw of background difference algorithm. The mathematical morphology method is employed to denoise the image, which may be helpful to improve the accuracy of the detection. The Pyramid algorithm is used to compress each frame data of video sequence. Then, the detecting and tracking of moving objects are tested on the hardware platform (DM643) and the software frame (RF5). The running speed is about 3 times faster than before. The result shows that the accuracy demanded by the detection is met. This method can provide a useful reference for similar application.

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44

Li, Jinling, Qingshan Hou, and Jinsheng Xing. "Multiobject Detection Algorithm Based on Adaptive Default Box Mechanism." Complexity 2020 (August 28, 2020): 1–11. http://dx.doi.org/10.1155/2020/5763476.

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Multiobject detection tasks in complex scenes have become an important research topic, which is the basis of other computer vision tasks. Considering the defects of the traditional single shot multibox detector (SSD) algorithm, such as poor small object detection effect, reliance on manual setting for default box generation, and insufficient semantic information of the low detection layer, the detection effect in complex scenes was not ideal. Aiming at the shortcomings of the SSD algorithm, an improved algorithm based on the adaptive default box mechanism (ADB) is proposed. The algorithm introduces the adaptive default box mechanism, which can improve the imbalance of positive and negative samples and avoid manually set default box super parameters. Experimental results show that, compared with the traditional SSD algorithm, the improved algorithm has a better detection effect and higher accuracy in complex scenes.

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45

Brogan, William L. "Algorithm for ranked assignments with applications to multiobject tracking." Journal of Guidance, Control, and Dynamics 12, no. 3 (May 1989): 357–64. http://dx.doi.org/10.2514/3.20416.

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46

Wada, T., and T. Matsuyama. "Multiobject behavior recognition by event driven selective attention method." IEEE Transactions on Pattern Analysis and Machine Intelligence 22, no. 8 (2000): 873–87. http://dx.doi.org/10.1109/34.868687.

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47

Chien, Li-Hsin, Joshua E. Barnes, Lisa J. Kewley, and Kenneth C. Chambers. "Multiobject Spectroscopy of Young Star Clusters in NGC 4676." Astrophysical Journal 660, no. 2 (April 11, 2007): L105—L108. http://dx.doi.org/10.1086/518215.

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48

Zhou, Hanying. "Improved interclass multiobject discrimination with phase-difference prewhitening technique." Optical Engineering 32, no. 11 (1993): 2720. http://dx.doi.org/10.1117/12.150681.

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49

Chen, Xinjian, Jayaram K. Udupa, Abass Alavi, and Drew A. Torigian. "Automatic anatomy recognition via multiobject oriented active shape models." Medical Physics 37, no. 12 (November 23, 2010): 6390–401. http://dx.doi.org/10.1118/1.3515751.

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., Sonali Deshmukh. "REVIEW PAPER ON SEGMENTATION METHODS FOR MULTIOBJECT FEATURE EXTRACTION." International Journal of Research in Engineering and Technology 03, no. 12 (December 25, 2014): 64–67. http://dx.doi.org/10.15623/ijret.2014.0312009.

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