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 | ||
| Volume-29 Number-1 |
||
| 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.
Abstract -
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.
References
[1]. 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.
[2]. Feature point correspondence in the presence of occlusion
SALARI, V. AND SETHI, I. K. 1990. IEEE
[3]. Establishing motion correspondence RANGARAJAN, K. AND
SHAH,M. 1991. Conference Vision Graphies Image Process
[4]. Real-time closed-world tracking INTILLE, S.,DAVIS, J.,
ANDBOBICK,A. 1997. IEEE
[5]. Resolving motion correspondence for densely moving points
VEENMAN, C., REINDERS, M., AND BACKER, E. 2001.. IEEE
[6]. A non-iterative greedy algorithm for multi-frame point
correspondence .SHAFIQUE, K. AND SHAH, M. 2003 In IEEE
[7]. 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.
[8]. 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.
[9]. 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.
[10]. 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.
[11]. 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
[12]. 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.
[13]. 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
[14]. 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
[15]. 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
[16]. “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.”
[17]. 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
[18]. 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
[19]. Z. Han, Q. Ye, J. Jiao, Combined feature evaluation for adaptive
visual object tracking, Comput. Vis. Image Understand. 115 (2011)
69–80
[20]. 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.
[21]. Hierarchical Kalman-particle filter with adaptation to motion
changesfor object tracking Shimin Yin, Jin Hee Na, Jin Young Choi,
Songhwai Oh 2011
[22]. A parallel histogram-based particle filter for object tracking on
SIMD-based smart camerasHenry Medeiros *, Germán Holguín, Paul
J. Shin, Johnny Park
[23]. 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
[24]. 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
[25]. 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.
[26]. 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
[27]. Andreas Opelt1, Axel Pinz1, and Andrew Zisserman2 A
Boundary-Fragment-Model for Object Detection.IEEE
[28]. Krystian Mikolajczyk Bastian Leibe Bernt Schiele Multiple
Object Class Detection with a Generative Model. IEEE
[29]. Krystian Mikolajczyk et al Pedestrian Detection in Crowded
Scenes.IEEE
[30]. 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
Keywords
Multi object tracking, Multi object
detection, video surveillance system.