A Survey of Image Processing Techniques for Identification and Tracking of Objects from a Video Sequence

A Survey of Image Processing Techniques for Identification and Tracking of Objects from a Video Sequence

  IJCOT-book-cover
 		 International Journal of Computer & Organization Trends  (IJCOT)          
 
© 2014 by IJCOT Journal
Volume - 4 Issue - 2
Year of Publication : 2014
Authors :  Farhana Wani , Adeel Ahmed khan , Dr. S Basavaraj Patil
DOI :  10.14445/22492593/IJCOT-V6P306

Citation

Farhana Wani , Adeel Ahmed khan , Dr. S Basavaraj Patil. "A Survey of Image Processing Techniques for Identification and Tracking of Objects from a Video Sequence", International Journal of Computer & organization Trends (IJCOT), V4(2):24-29 Mar - Apr 2014, ISSN:2249-2593, www.ijcotjournal.org. Published by Seventh Sense Research Group.

Abstract

Visual surveillance is an emerging research topic in image processing. This paper discusses about various image processing techniques and tools which are available for identification and tracking of moving objects in a crowd. In general, the processing framework of visual surveillance task includes the following stages: detection of moving objects, their classification, tracking, and identification of the behavior. In this paper, we provide a brief overview of the techniques for object detection and tracking as well as some insights to the behavior of the crowd. Object detection and tracking algorithms can be proactively used to respond to accidents, crime, suspicious activities, terrorism, and may provide insights to improve evacuation planning and real-time situation awareness during public disturbances. For the visual surveillance task, the virtual analyst has to work on the information provided by the detection and tracking segment of the system and based on that, it has to discover some interesting patterns within a crowd.

References

[1] N. Sulman, T. Sanocki, D. Goldgof, and R. Kasturi, “How effective is human video surveillance performance?” in Proc. Int. Conf. Pattern Recog., 2008, pp. 1–3.
[2] Z. Ghahramani. Learning dynamic bayesian networks. In Adaptive Processing of Sequences and Data Structures. Lecture Notes in AI, pages 168–197, 1998.
[3] D. Heckerman. A tutorial on learning with Bayesian networks. Technical Report MSR-TR-95-06, Microsoft Research, 1995.
[4] M. Brand and V. Kettnaker. Discovery and segmentation of activities in video. PAMI, 22(8):844–851, August 2000.
[5] C. Vogler and D. Metaxas. A framework for recognizing the simultaneous aspects of American Sign Language. CVIU, 81:358–384, 2001.
[6] M. Brand, N. Oliver, and A. Pentland. Coupled hidden markov models for complex action recognition. In CVPR, pages 994–999, Puerto Rico,
[7] N. Oliver, B. Rosario, and A. Pentland. A bayesian computer vision system for modeling human interactions.PAMI,22(8):831–843, August 2000.
[8] Z. Ghahramani. Learning dynamic bayesian networks.In Adaptive Processing of Sequences and Data Structures. Lecture Notes in AI, pages 168–197, 1998.
[9] Anthony Hoogs Steve Bush Glen Brooksby
[10] T. Zhao and R. Nevatia, “Bayesian Human Segmentation in Crowded Situations,” Proc. IEEE CS Conf. Computer Vision and Pattern Recognition, pp. 459-466, 2003.
[11] G.M.Q. Yu and I. Cohen, “Multiple Target Tracking Using Spatio-Temporal Monte Carlo Markov Chain Data Association,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2007.
[12] W. Ge and R.T. Collins, “Marked Point Processes for Crowd Counting,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2009.
[13] N. Dalal and B. Triggs, “Histograms of Oriented Gradients for Human Detection,” Proc. IEEE CS Conf. Computer Vision and Pattern Recognition, pp. 886-893, 2005.
[14] P. Viola, M. Jones, and D. Snow, “Detecting Pedestrians Using Patterns of Motion and Appearance,” Proc. IEEE Int’l Conf. Computer Vision, pp. 734-741, 2003.
[15] J. Heikkila and O. Silven, “A real-time system for monitoring of cyclists and pedestrians,” in Proc. IEEE Workshop Visual Surveillance, 1999,pp. 74–81.
[16] C. Stauffer andW. E. L. Grimson, “Adaptive background mixture models for real-time tracking,” in Proc. IEEE Int. Conf. Comput. Vis. PatternRecog., 1999, vol. 2, pp. 246–252.
[17] G. Halevy and D. Weinshall, “Motion of disturbances: Detection and tracking of multi-body non-rigid motion,” in Proc. IEEE Int. Conf. Comput.Vis. Pattern Recog., 1997, pp. 897–902.
[18] R. Cutler and L. Davis, “View-based detection and analysis of periodic motion,” in Proc. Int. Conf. Pattern Recog., 1998, pp. 495–500.
[19] K. Toyama, J. Krumm, B. Brumitt, and B. Meyers, “Wallflower: Principles and practice of background maintenance,” in Proc. IEEE Int. Conf.Comput. Vis., 1999, pp. 255–261.
[20] R. J. Radke, S. Andra, O. Al-Kofahi, and B. Roysam, “Image change detection algorithms: A systematic survey,” IEEE Trans. Image Process.,vol. 14, no. 3, pp. 294–307, Mar. 2005.
[21] N. Sulman, T. Sanocki, D. Goldgof, and R. Kasturi, “How effective is human video surveillance performance?” in Proc. Int. Conf. PatternRecog., 2008, pp. 1–3.
[22] F. Yin, D. Makris, and S. A. Velastin, “Performance evaluation of object tracking algorithms,” in Proc. IEEE Int. Workshop Perform.Eval.Tracking Surveillance, 2007, pp. 733–736.
[23] S. Muller-Schneiders, T. Jager, H. S. Loos, and W. Niem, “Performance evaluation of a real time video surveillance system,” in Proc. IEEE Int. Workshop Visual Surveillance Perform. Eval.Tracking Surveillance, 2005, pp. 137–143.
[24] T. List, J. Bins, J. Vazquez, and R. B. Fisher, “Performance evaluating the evaluator,” in Proc. IEEE Int. Workshop Visual Surveillance Perform.Eval.Tracking Surveillance, 2005, pp. 129–136.
[25] F. Ziliani, S. A. Velastin, F. Porikli, L. Marcenaro, T. Kelliher, A. Cavallaro, and P. Bruneaut, “Performance evaluation of event detection solutions: The CREDS experience,” in Proc. IEEE Conf. Adv. Video Signal Based Surveillance, 2005, pp. 201–206.
[26] K. Karmann and A. Brandt, “Moving object recognition using an adaptive background memory,” in Time-Varying Image Processing and Moving Object Recognition, V. Cappellini, Ed. Amsterdam, The Netherlands: Elsevier, 1990, vol. 2.
[27] M. Kilger, “A shadow handler in a video-based real-time traffic monitoring system,” in Proc. IEEE Workshop Applications of Computer Vision, Palm Springs, CA, 1992, pp. 11–18.
[28] C. R. Wren, A. Azarbayejani, T. Darrell, and A. P. Pentland, “Pfinder: real-time tracking of the human body,” IEEE Trans. Pattern Anal. Machine Intell., vol. 19, pp. 780–785, July 1997.
[29] S. McKenna, S. Jabri, Z. Duric, A. Rosenfeld, and H. Wechsler, “Tracking groups of people,” Comput. Vis. Image Understanding, vol.80, no. 1, pp. 42–56, 2000.
[30] JPL, “Traffic surveillance and detection technology development” Sensor Development Final Rep., Jet Propulsion Laboratory Publication no. 97-10, 1997.
[31] J. Malik, S. Russell, J. Weber, T. Huang, and D. Koller, “A machine vision based surveillance system for Californaia roads,” Univ. of California, PATH project MOU-83 Final Rep., Nov. 1994.
[32] A. Baumberg and D. C. Hogg, “Learning deformable models for tracking the human body,” in Motion-Based Recognition, M. Shah andR. Jain, Eds. Norwell, MA: Kluwer, 1996, pp. 39–60.
[33] A. Mohan, C. Papageorgiou, and T. Poggio, “Example-based object detection in images by components,” IEEE Trans. Pattern Recognit. Machine Intell., vol. 23, pp. 349–361, Apr. 2001.
[34] A. Galata, N. Johnson, and D. Hogg, “Learning variable-length Markov models of behavior,” Comput. Vis. Image Understanding, vol. 81, no. 3, pp. 398–413, 2001.
[35] Y. Wu and T. S. Huang, “A co-inference approach to robust visual tracking,” in Proc. Int. Conf. Computer Vision, vol. II, 2001, pp. 26–33.
[36] N. Peterfreund, “Robust tracking of position and velocity with Kalmansnakes,” IEEE Trans. Pattern Anal. Machine Intell., vol. 22, pp.564–569, June 2000.
[37] M. Isard and A. Blake, “Contour tracking by stochastic propagation of conditional density,” in Proc. European Conf. Computer Vision, 1996, pp. 343–356.
[38] D. Koller, J. Weber, T. Huang, J. Malik, G. Ogasawara, B. Rao, and S.Russel, “Toward robust automatic traffic scene analysis in real-time,” in Proc. Int. Conf. Pattern Recognition, Israel, 1994, pp. 126–131.
[39] J. Malik and S. Russell, “Traffic Surveillance and Detection Technology Development: New Traffic Sensor Technology,” Univ. of California, Berkeley, California PATH Research Final Rep., UCB-ITS-PRR-97-6, 1997.
[40] C. A. Pau and A. Barber, “Traffic sensor using a color vision method,” in Proc. SPIE—Transportation Sensors and Controls: Collision Avoidance, Traffic Management, and ITS, vol. 2902, 1996, pp. 156–165.
[41] B. Schiele, “Vodel-free tracking of cars and people based on color regions,” in Proc. IEEE Int. Workshop Performance Evaluation of Tracking and Surveillance, Grenoble, France, 2000, pp. 61–71.
[42] R. Polana and R. Nelson, “Low level recognition of human motion,” in Proc. IEEE Workshop Motion of Non-Rigid and Articulated Objects, Austin, TX, 1994, pp. 77–82.
[43] B. Coifman, D. Beymer, P. McLauchlan, and J. Malik, “A real-time computer vision system for vehicle tracking and traffic surveillance,” Transportation Res.: Part C, vol. 6, no. 4, pp. 271–288, 1998.
[44] J. Malik and S. Russell, “Traffic surveillance and detection technology development (new traffic sensor technology),” Univ. of California, Berkeley, 1996.
[45] T. J. Fan, G. Medioni, and G. Nevatia, “Recognizing 3-D objects using surface descriptions,” IEEE Trans. Pattern Recognit. Machine Intell., vol. 11, pp. 1140–1157, Nov. 1989.
[46] R.W. Brown, “Mass Phenomena,” Handbook of SocialPsychology, G. Lindzey, ed., vol. 2, pp. 833-876, Addison Wesley, 1954.
[47] G. Still, “Crowd Dynamics,” PhD thesis, Univ. of Warwick, 2000.
[48] D. Cartwright and A. Zander, Group Dynamics: Research and Theory, third ed. Harper, 1968.
[49] N.R. Johnson, “Panic at the Who Concert Stampede: An Empirical Assessment,” Social Problems, vol. 34, pp. 362-373, 1987.
[50] A. Aveni, “The Not-So-Lonely Crowd: Friendship Groups in Collective Behavior,” Sociometry, vol. 49, pp. 96-99, 1977.
[51] C. McPhail, “Withs across the Life Course of Temporary Sport Gatherings,” unpublished manuscript, Univ. of Illinois, 2003.
[52] M. Brand, N. Oliver, and A. Pentland, “Coupled hidden Markov models for complex action recognition,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition, 1997, pp. 994–999.
[53] T. Starner, J. Weaver, and A. Pentland, “Real-time American sign language recognition using desk and wearable computer-based video,”IEEE Trans. Pattern Anal. Machine Intell., vol. 20, pp. 1371–1375,Dec. 1998.
[54] N. M. Oliver, B. Rosario, and A. P. Pentland, “A Bayesian computer vision system for modeling human interactions,” IEEE Trans. Pattern Anal. Machine Intell., vol. 22, pp. 831–843, Aug. 2000.
[55] M. Brand and V. Kettnaker, “Discovery and segmentation of activities in video,” IEEE Trans. Pattern Anal. Machine Intell., vol. 22, pp. 844–851,Aug. 2000.
[56] M. Ryoo and J. Aggarwal, “Recognition of High-Level Group Activities Based on Activities of Individual Members,” Proc. IEEE Workshop Motion and Video Computing, pp. 1-8, Jan. 2008.
[57] W. Zhang, F. Chen, W. Xu, and Y. Du, “Hierarchical Group Process Representation in Multi-Agent Activity Recognition,” Image Comm., vol. 23, pp. 739-739, Jan. 2008.
[58] A. May, Traffic Flow Fundamental. Prentice Hall, 1990.
[59] D. Helbing and P. Molnar, “Social Force Model for Pedestrian Dynamics,” Physical Rev. E, vol. 51, no. 5, pp. 4282-4286, 1995.
[60] P. Scovanner and M. Tappen, “Learning Pedestrian Dynamics from the Real World,” Proc. IEEE Int’l Conf. Computer Vision, 2009.
[61] S. Pellegrini, A. Ess, K. Schindler, and L. van Gool, “You’ll Never Walk Alone: Modeling Social Behavior for Multi-Target Tracking,”
[62] R. Mehran, A. Oyama, and M. Shah, “Abnormal Crowd Behavior Detection Using Social Force Model,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2009.
[63] V. Blue and J. Adler, “Cellular Automata Microsimulation for Modeling Bi-Directional Pedestrian Walkways,” Transportation Research B, vol. 35, no. 3, pp. 293-312, 2001.
[64] A. Schadschneider, “Cellular Automaton Approach to Pedestrian Dynamics—Theory,” Proc. Int’l Conf. Pedestrian and Evacuation Dynamics, pp. 75-86, 2002.
[65] Shaogang Gong and Tao Xiang,“Recognition of Group Activities using Dynamic Probabilistic Networks,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2009.
[66] S. Ali and M. Shah, “Floor Fields for Tracking in High Density Crowd Scenes,” Proc. European Conf. Computer Vision, pp. 1-14, Oct. 2008.

Keywords
Visual surveillance, motion detection, classification, tracking, behavior recognition, Hidden Markov Model