A Review on Multiple Objects Tracking in a Video Scene with Particle Filtering Techniques

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2019 by IJCTT Journal
Volume-67 Issue-6
Year of Publication : 2019
Authors : Prodip Kumar Sarker, Dr. Sumon Kumar Debnath, Md. Jamal Uddin
DOI :  10.14445/22312803/IJCTT-V67I6P110

MLA

MLA Style:Prodip Kumar Sarker, Dr. Sumon Kumar Debnath, Md. Jamal Uddin"A Review on Multiple Objects Tracking in a Video Scene with Particle Filtering Techniques" International Journal of Computer Trends and Technology 67.6 (2019): 65-69.

APA Style Prodip Kumar Sarker, Dr. Sumon Kumar Debnath, Md. Jamal Uddin. A Review on Multiple Objects Tracking in a Video Scene with Particle Filtering TechniquesInternational Journal of Computer Trends and Technology, 67(6),65-69.

Abstract
Multiple objects tracking in a video scene is one of the most challenging tasks in the field of computer vision as well as it is highly demand in many computer applications, such as surveillance, tracking of animals, pedestrians, vehicles, human behavior analysis, and so on. A complex scene is characterized by several moving objects such as people, animals, vehicles, etc. In order to perform higher level tasks, such as to fight against terrorism, crime, public safety for efficient management of traffic, etc. which are demanded by typical surveillance or monitoring applications, to detect, identify and track various objects of interest. There are many approaches have been proposed to solve this problem, it still remains challenging due to factors like abrupt appearance changes and severe object occlusions. The objectives of multiple object tracking are to place moving objects in sequential video frames. In this paper, we contribute the first comprehensive and most recent review on this problem using Particle filters. We provide a discussion about issues of multiple objects tracking via Particle filters research, as well as some interesting directions which could possibly become potential research effort in the future

Reference
[1] B. Yang, C. Huang, and R. Nevatia, “Learning affinities and dependencies for multi-target tracking using a CRF model,” in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., pp. 1233–1240, 2011.
[2] S. Pellegrini, A. Ess, K. Schindler, and L. Van Gool, “You’ll never walk alone: Modeling social behavior for multi-target tracking,” in Proc. IEEE Int. Conf. Comput. Vis, pp. 261–268, 2009.
[3] D. Koller, J. Weber, and J. Malik, “Robust multiple car tracking with occlusion reasoning,” in Proc. Eur. Conf. Comput. Vis. pp. 189–196, 1994.
[4] M. Betke, E. Haritaoglu, and L. S. Davis, “Real-time multiple vehicle detection and tracking from a moving vehicle,” Mach. Vis. Appl., vol. 12, no. 2, pp. 69–83, Feb. 2000.
[5] W.-L. Lu, J.-A. Ting, J. Little, and K. Murphy, “Learning to track and identify players from broadcast sports videos,” IEEE Trans. Pattern Anal. Mach. Intel., vol. 35, no. 7, pp. 1704–1716, Jul. 2013.
[6] J. Xing, H. Ai, L. Liu, and S. Lao, “Multiple player tracking in sports video: a dual-mode two-way bayesian inference approach with progressive observation modeling,” IEEE Tran. Image Process., vol. 20, no. 6, pp. 1652–1667, Jun. 2011.
[7] P. Nillius, J. Sullivan, and S. Carlsson, “Multi-target tracking linking identities using bayesian network inference,” in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., 2006, pp. 2187–2194.
[8] W. Luo, T.-K. Kim, B. Stenger, X. Zhao, and R. Cipolla, “Bi-label propagation for generic multiple object tracking,” in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., pp. 1290–1297, 2014.
[9] M. Betke, D. E. Hirsh, A. Bagchi, N. I. Hristov, N. C. Makris, and T. H. Kunz, “Tracking large variable numbers of objects in clutter,” in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit, pp. 1–8, 2007.
[10] Z. Khan, T. Balch, and F. Dellaert, “An MCMC-based particle filter for tracking multiple interacting targets,” in Proc. Eur. Conf. Comput. Vis, pp. 279–290, 2004.
[11] C. Spampinato, S. Palazzo, D. Giordano, I. Kavasidis, F.-P. Lin, and Y.-T. Lin, “Covariance based fish tracking in real-life underwater environment,” in Proc. Int. Conf. Comput. Vis. Theory Appl., pp. 409–414, 2012.
[12] E. Fontaine, A. H. Barr, and J.W. Burdick, “Model-based tracking of multiple worms and fish,” in Proc. IEEE Int. Conf. Comput. Vis. Workshops, pp. 1–13, 2007.
[13] E. Meijering, O. Dzyubachyk, I. Smal, and W. A. van Cappellen, “Tracking in cell and developmental biology” Semin. Cell Dev. Biol., vol. 20, no. 8, pp. 894–902, Aug. 2009.
[14] K. Li, E. D. Miller, M. Chen, T. Kanade, L. E. Weiss, and P. G. Campbell, “Cell population tracking and lineage construction with spatiotemporal context,” Med. Image Anal., vol. 12, no. 5, pp. 546–566, May 2008.
[15] G. Duan, H. Ai, S. Cao, and S. Lao, “Group tracking: exploring mutual relations for multiple object tracking,” in Proc. Eur. Conf. Comput. Vis., pp. 129–143, 2012.
[16] T. Pfister, J. Charles, and A. Zisserman, “Flowing convnets for human pose estimation in videos,” in Proc. IEEE Int. Conf. Comput. Vis., pp. 1913–1921, 2015.
[17] W. Choi and S. Savarese, “A unified framework for multi-target tracking and collective activity recognition,” in Proc. Eur. Conf. Comput. Vis., pp. 215–230, 2012.
[18] W. Hu, T. Tan, L. Wang, and S. Maybank, “A survey on visual surveillance of object motion and behaviors,” IEEE Trans. Syst. Man Cybern. Part C-Appl. Rev., vol. 34, no. 3, pp. 334–352, Mar. 2004.
[19] X.Wang, “Intelligent multi-camera video surveillance: A review,” Pattern Recognit. Lett., vol. 34, no. 1, pp. 3–19, Jan. 2013.
[20] J. Candamo, M. Shreve, D. B. Goldgof, D. B. Sapper, and R. Kasturi, “Understanding transit scenes: A survey on human behavior-recognition algorithms,” IEEE Trans. Intell. Transp. Syst., vol. 11, no. 1, pp. 206–224, Jan. 2010.
[21] H. Uchiyama and E. Marchand, “Object Detection and Pose Tracking for Augmented Reality: Recent Approaches,” in Proc. Korea-Japan Joint Workshop Frontiers Comput. Vis., pp. 721–730, 2012.
[22] Yilmaz Alper, Javed Omar and Shah Mubarak, “Object Tracking: A survey” ACM Compt. Surv.38, 4, Article 13, pp.1-45, December, 2006.
[23] Da Tang, Yu-Jin Zhang, "Combining Mean-shift and Particle Filter for Object Tracking", 2011 Sixth International Conference on Image and Graphics, 978-0-7695-4541-7/11 $26.00 IEEE DOI 10.1109/ICIG.2011.118 , 2011.
[24] Sandeep Kumar Patel, Agya Mishra "Moving Object Tracking Techniques: A Critical Review", Indian Journal of Computer Science and Engineering (IJCSE) ISSN : 0976-5166 Vol. 4 No.2, pp. 95-102 , Apr-May 2013 .
[25] Jaward, M., Mihaylova, L., Canagarajah, N., & Bull, D. (n.d.). “Multiple Object Tracking Using Particle Filters”, IEEE Aerospace Conference, 2006,
[26] Li P., Wang H. “Object Tracking with Particle Filter Using Color Information”, In: Gagalowicz A., Philips W. (eds) Computer Vision/Computer Graphics Collaboration Techniques. Lecture Notes in Computer Science, vol 4418. Springer, Berlin, Heidelberg, MIRAGE, pp 534-541, 2007.
[27] Beiji Zou, Xiaoning Peng and Liqin Han, Particle Filter With Multiple Motion Models For Object Tracking In Diving Video Sequences, IEEE, 224-228, 2008.
[28] Md. Zahidul Islam and Chil-Woo Lee, “Adaptive Template Based Object Tracking with Particle Filter”, 5th International Conference on Electrical and Computer Engineering ICECE 2008, 20-22 December 2008.
[29] J. Kim, Z. Lin, and I. S. Kweon, “Rao-Blackwellized particle filtering with Gaussian mixture models for robust visual tracking,” Computer Vision and Image Understanding, vol. 125, pp. 128–137, 2014.
[30] M.-L. Gao, L.-L. Li, X.-M. Sun, L.-J. Yin, H.-T. Li, and D.-S. Luo, “Firefly algorithm (FA) based particle filter method for visual tracking,” Optik - International Journal for Light and Electron Optics, vol. 126, no. 18, pp. 1705–1711, 2015.
[31] Z. Fan, H. Ji, and Y. Zhang, “Iterative particle filter for visual tracking,” Signal Processing: Image Communication, vol. 36, pp. 140–153, 2015.
[32] Sanjay S. Sakharkar, S. D. Kamble, Dr. A.S.Khobragade, “Object Detection and Tracking Using Particle Filtering”, International Journal of Computer Trends and Technology (IJCTT) – volume 22, Number 1, pp. 16-19, April 2015.
[33] Hamd Ait Abdelali, Fedwa Essannouni and Driss Aboutajdine, “Object tracking in video via particle filter”, Int. J. Intelligent Engineering Informatics, Vol. 4, Nos. 3/4, 2016.
[34] W. Li, P. Wang, and H. Qiao, “Top-down visual attention integrated particle filter for robust object tracking,” Signal Processing: Image Communication, vol. 43, pp. 28–41, 2016.
[35] Pirayawaraporn, A., Chindakham, N., & Jeong, M.-H. Alongkorn Pirayawaraporn, Nachaya Chindakham and Mun-Ho Jeong, “Object Tracking Using Particle Filter with Back Projection-BasedSampling on Saliency”, 17th International Conference on Control, Automation and Systems (ICCAS 2017) pp. 18–21, Oct, 2017.
[36] Ghasemi, C. N. Ravi Kumar, “A Survey of Multi Object Tracking and Detecting Algorithm in Real Scene use in video surveillance systems”, International Journal of Computer Trends and Technology (IJCTT) – volume 29, Number 1, pp. 31-39, November 2015.

Keywords
Object tracking, Computer vision, Particle filter, Video scene.