(Research & Development Centre, Bharathiar University, Coimbatore, India)(2) Balakrishnan Ganesan
(Indra Ganesan College of Engineering, Tiruchirappalli, India)*corresponding author
AbstractMany applications such as intelligent transportation, video surveillance, robotics of computer vision mainly depend on task of multiple object tracking. It includes the process of detection, classifications and tracking. The main focus of the study is to develop an efficient and effective multiple target tracking methods to solve the issues of illumination changes, occlusions and affinity matching. Accordingly, the various multiple target tracking methods are tested and evaluated using the metrics on publicly available datasets from which it is obvious that the outcome of the global energy minimization and optimization techniques is comparatively better than any other existing techniques in all aspects. This comparative study work will also help in better understanding of the problem, knowledge of the methods and experimental evaluation skill for further research works.
|
DOIhttps://doi.org/10.26555/ijain.v2i2.55 |
Article metricsAbstract views : 1461 | PDF views : 200 |
Cite |
Full Text Download
|
References
J. Black, T. Ellis, and P. Rosin. Multi view image surveillance and tracking. In Motion & Video Computing Workshop, Dec. 2002.
D. Reid. An algorithm for tracking multiple targets. IEEE Transactions on Automatic Control, 24(6):843–854, Dec. 1979.
K. Okuma, A. Taleghani, O. D. Freitas, J. J. Little, and D. G. Lowe. A boosted particle filter: Multitarget detection and tracking. In ECCV 2004, volume 1, pages 28–39.
J. Vermaak, A. Doucet, and P. Perez. Maintaining multi-modality through mixture tracking. In ICCV 2003.
T. E. Fortmann, Y. Bar-Shalom, and M. Scheffe. Multi-target tracking using joint probabilistic data association. In IEEE Conf. on Decision and Control, volume 19, pages 807–812, Dec. 1980.
Michael Kamaraj and Balakrishnan. An improved motion detection and tracking of active blob for video surveillance, 4th ICCCNT 2013, ISBN: 978-1-4799-3927-5, Page 569-575.
B. Leibe, K. Schindler, and L. Van Gool. Coupled detection and trajectory estimation for multi-object tracking. In ICCV 2007.
Michael Kamaraj and Balakrishnan. Surveillance of human tracking using Gaussian beta-likelihood matching and kalman filter, International Journal of Applied Engineering Research ISSN 0973-4562 Volume 10, Number 14 (2015) pp 34375-34382 © Research India Publications .Available at: http://www.ripublication.com.
Michael Kamaraj and Balakrishnan. Human Motion tracking using Gaussian Mixture model and Beta- likelihood matching, IOSR Journal of Computer Engineering (IOSR-JCE), e-ISSN: 2278-0661, p-ISSN: 2278-8727 PP 40-48.
H. Jiang, S. Fels, and J. J. Little. A linear programming approach for multiple object tracking. In CVPR 2007.
Michael Kamaraj and Balakrishnan. Global energy minimization and optimization of multi-target tracking. 2nd ICEEET16, March 2016.
Michael Kamaraj and Balakrishnan. Optimization of multi-target and Occlusion handling using mean shift method, International Journal of Advanced Research in Computer Science and Software Engineering, ISSN: 2277 128X , Volume 5, Issue 9, September 2015.
J. Berclaz, F. Fleuret, and P. Fua. Multiple object tracking using flow linear programming. In winter-PETS, Dec. 2009.
M. Rodriguez, I. Laptev, J. Sivic, and J.-Y. Audibert. Density-aware person detection and tracking in crowds. In ICCV 2011.
J. Xing, H. Ai, and S. Lao. Multi-object tracking through occlusions by local tracklets filtering and global tracklets association with detection responses. In CVPR 2009.
L. Kratz and K. Nishino. Tracking with local spatio-temporal motion patterns in extremely crowded scenes. In CVPR 2010.
Choi W, Savarese S, Multiple target tracking in world coordinate with single, minimally calibrated camera. In:Proc. Eur. Conf. Comput. Vis., 2010, pp 553-567.
Qin Z, Shelton CR, Improving multi-target tracking via social grouping. In: Proc. IEEE Int. Conf. Comput.Vis. Pattern Recognit. 2012, pp 1972-1978.
Zhang L, Li Y, Nevatia R, Global data association for multi-object tracking using network flows. In:Proc. IEEE Int. Conf. Comput. Vis. Pattern Recognit. 2008, pp 1-8.
Henriques JF, Caseiro R, Batista J, Globally optimal solution to multi-object tracking with merged measurements. In: Proc. IEEE Int. Conf. Comput. Vis. 2011, pp 2470-2477.
Breitenstein MD, Reichlin F, Leibe B, Koller-Meier E, Van Gool L , Robust tracking-by-detection using a detector confidence particle filter. In: Proc. IEEE Int. Conf. Comput. Vis. 2009,pp 1515-1522.
Andriyenko A, Schindler K , Multi-target tracking by continuous energy minimization. In: Proc. IEEE Int. Conf. Comput. Vis. Pattern Recognition 2011, pp 1265-1272.
Milan A, Roth S, Schindler K, Continuous energy minimization for multi-target tracking. IEEE Trans. Pattern Anal Mach Intel. 2014, 36(1):58-72.
Bae SH, Yoon KJ, Robust online multi-object tracking based on tracklet confidence and online discriminative appearance learning. In: Proc. IEEE Int. Conf. Comput. Vis. Pattern Recognit. 2014, pp 1218-1225.
B. Wu and R. Nevatia, Tracking of multiple, partially occluded humans based on static body part detection, In CVPR, 2006.
Stauffer C., and Grimson W., “Adaptive Background Mixture Models for Real-Time Tracking” in Proceedings of IEEE Computer Vision and Pattern Recognition, Vol. 2, 1999, pp.246-252.
B.Z. de Villiers, W.A.Clarke, P.E. Robinson, “Mean shift Object Tracking with Occlusion Handling”, Available at: www.prasa.org/proceedings/2012/prasa2012-36.pdf.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
___________________________________________________________
International Journal of Advances in Intelligent Informatics
ISSN 2442-6571 (print) | 2548-3161 (online)
Organized by UAD and ASCEE Computer Society
Published by Universitas Ahmad Dahlan
W: http://ijain.org
E: info@ijain.org (paper handling issues)
andri.pranolo.id@ieee.org (publication issues)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0