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)||
|© 2015 by IJCTT Journal|
|Year of Publication : 2015|
|Authors : Abouzar Ghasemi, C.N Ravi Kumar|
|DOI : 10.14445/22312803/IJCTT-V29P107|
Abouzar 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) V29(1):31-39, October 2015. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.
There are now large networks of CCTV cameras collecting great amounts of image data, many of which deploy Pan-Tilt-Zoom (PTZ) controllable cameras. A multi-camera and multi-sensor system has potential both for gaining improved imaging quality and for capturing more relevant details .Such a system can also cause overflow of information and confusion if data content is not analyzed in real-time. Video Analytics is the emerging technology where Computer Vision and Pattern Recognition techniques are used to filter and manage real time CCTV videos for security and intelligent monitoring. Background subtraction, object detection, object tracking, re-identification, and behavior analysis are the most important components for a Video Analytics system. The scientist has some of the cutting edge technologies in this area, which exploit recent statistical and differential geometric theories and adapt them to challenging tasks for example, individuating eye directions, tracking groups of people, re-identifying individuals in different days that take place in real case scenarios. Detecting and tracking human beings in a given scene represents one of the most important and challenging tasks in computer vision. We are interested to consider some of powerful methods in these issues for their implications in videosurveillance and driver assistance systems.
. SETHI, I. AND JAIN, R. 1987. Finding trajectories of feature points in a monocular image sequence. IEEE Trans. Patt. Analy. Mach. Intell. 9, 1, 56–73.
. Feature point correspondence in the presence of occlusion SALARI, V. AND SETHI, I. K. 1990. IEEE
. Establishing motion correspondence RANGARAJAN, K. AND SHAH,M. 1991. Conference Vision Graphies Image Process
. Real-time closed-world tracking INTILLE, S.,DAVIS, J., ANDBOBICK,A. 1997. IEEE
. Resolving motion correspondence for densely moving points VEENMAN, C., REINDERS, M., AND BACKER, E. 2001.. IEEE
. A non-iterative greedy algorithm for multi-frame point correspondence .SHAFIQUE, K. AND SHAH, M. 2003 In IEEE
. I. Haritaoglu, D. Harwood, L.S. David, W4: Real-time surveillance of people and their activities, IEEE Trans. Pattern Anal. Mach. Intell. 22 (2000) 809–830.
. Hwann-Tzong Chen, Horng-Horng Lin, T.-L. Liu, Multi-object tracking using dynamical graph matching, in: Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, vol. 2, 2001, pp. 210– 217.
. C. Dai, Y. Zheng, X. Li, Pedestrian detection and tracking in infrared imagery using shape and appearance, Comput. Vis. Image Understand. 106 (2007) 288–299.
. S. Pellegrini, A. Ess, K. Schindler, L. van Gool, You’ll never walk alone: Modeling social behavior for multi-target tracking, in: IEEE 12th International Conference on Computer Vision, 2009, pp. 261–268.
. Robust Multiperson Tracking from a Mobile Platform Andreas Ess, Student Member, IEEE, Bastian Leibe, Member, IEEE, Konrad Schindler, Member, IEEE, and Luc van Gool, Member, IEEE,2009
. J. Berclaz, F. Fleuret, E. Turetken, P. Fua, Multiple object tracking using kshortest paths optimization, IEEE Trans. Pattern Anal. Mach. Intell. 33 (2011) 1806–1819.
. A real time algorithm for people tracking using contextual reasoning Rosario Di Lascio a, Pasquale Foggia b, Gennaro Percannella b, Alessia Saggese b,?, Mario Vento b 2013
. D. Comaniciu, V. Ramesh, P. Meer, Real-time tracking of nonrigid objects using mean shift, in: Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, vol. 2, 2000, pp. 142–149
. H. Tao, H. Sawhney, R. Kumar, Object tracking with bayesian estimation of dynamic layer representations, IEEE Trans. Pattern Anal. Mach. Intell. 24 (2002) 75–89
. “B. Wu, R. Nevatia, Detection of multiple, partially occluded humans in a single image by Bayesian combination of Edgelet part detectors, in: Tenth IEEE Int. Conf. on Computer Vision, vol. 1, 2005, pp. 90–97.”
. K. Bhuvaneswari, H. Abdul Rauf, Edgelet based human detection and tracking by combined segmentation and soft decision, in: International Conference on Control, Automation, Communication and Energy Conservation, INCACEC
. B. Yogameena, S. Roomi, S. Abhaikumar, Detecting and tracking people in a homogeneous environment using skin color model, in: Seventh Internationa Conference on Advances in Pattern Recognition, ICAPR ’09, 2009, pp. 282–285
. Z. Han, Q. Ye, J. Jiao, Combined feature evaluation for adaptive visual object tracking, Comput. Vis. Image Understand. 115 (2011) 69–80
. Y. Cai, N. de Freitas, J. Little, Robust visual tracking for multiple targets, in: A. Leonardis, H. Bischof, A. Pinz (Eds.), Computer Vision ECCV 2006, Lecture Notes in Computer Science, vol.
. Hierarchical Kalman-particle filter with adaptation to motion changesfor object tracking Shimin Yin, Jin Hee Na, Jin Young Choi, Songhwai Oh 2011
. A parallel histogram-based particle filter for object tracking on SIMD-based smart camerasHenry Medeiros *, Germán Holguín, Paul J. Shin, Johnny Park
. X. Song, J. Cui, H. Zha, H. Zhao, Vision-based multiple interacting targets tracking via on-line supervised learning, in: Proceedings of the 10th European Conference on Computer Vision: Part III, ECCV ’08, Springer-Verlag, Berlin
. M. Wang, H. Qiao, B. Zhang, A new algorithm for robust pedestrian tracking based on manifold learning and feature selection, IEEE Trans. Pattern Anal. Mach. Intell. 12 (2011) 1195–1208
. Yussiff, Abdul-Lateef, Suet-Peng Yong, and Baharum B. Baharudin. "Parallel Kalman filter-based multi-human tracking in surveillance video." Computer and Information Sciences (ICCOINS), 2014 International Conference on. IEEE, 2014.
. Michael D. Breitenstein Online Multiperson Tracking-by- Detection from a Single, Uncalibrated Camera IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 33, NO. 9, SEPTEMBER 2011
. Andreas Opelt1, Axel Pinz1, and Andrew Zisserman2 A Boundary-Fragment-Model for Object Detection.IEEE
. Krystian Mikolajczyk Bastian Leibe Bernt Schiele Multiple Object Class Detection with a Generative Model. IEEE
. Krystian Mikolajczyk et al Pedestrian Detection in Crowded Scenes.IEEE
. Amir Akramin Shafie et al. Smart Objects Identification System for Robotic Surveillance 11(1),February 2014, 59-71 DOI: 10.1007/s11633-014-0766-9.IEEE
Multi object tracking, Multi object detection, video surveillance system.