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An Insight into the Algorithms on Real-Time People Tracking and Counting System

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International Journal of Computer Applications
© 2012 by IJCA Journal
Volume 46 - Number 5
Year of Publication: 2012
Authors:
J. L. Raheja
Pallab Jyoti Dutta
Sishir Kalita
Solanki Lovendra
10.5120/6901-9257

J L Raheja, Pallab Jyoti Dutta, Sishir Kalita and Solanki Lovendra. Article: An Insight into the Algorithms on Real-Time People Tracking and Counting System. International Journal of Computer Applications 46(5):1-6, May 2012. Full text available. BibTeX

@article{key:article,
	author = {J. L. Raheja and Pallab Jyoti Dutta and Sishir Kalita and Solanki Lovendra},
	title = {Article: An Insight into the Algorithms on Real-Time People Tracking and Counting System},
	journal = {International Journal of Computer Applications},
	year = {2012},
	volume = {46},
	number = {5},
	pages = {1-6},
	month = {May},
	note = {Full text available}
}

Abstract

People tracking and counting system is being widely used in modern days in the video surveillance for security purpose. In the recent, many algorithms have been proposed and developed in designing the system. But there always been confusions of using these algorithms based on their limitations and benefits. This paper provides a review on the methods that are used in the people tracking and counting system considering their limitations, benefits, speed and accuracy. In designing an efficient real time people tracking and counting system, designers can be effectively guided by this review work.

References

  • Xiao Benxian, Lu Cheng, Chen Hao, Yu Yanfeng and Chen Rongbao, "Moving object detection and recognition based on the frame difference algorithm and moment invariant features", 27th Chinese Control Conference (CCC), pp. 578-581, 2008.
  • C. Wren, A. Azarbayejani, T. Darrell, and A. P. Pentland, "Pfinder: Real-time Tracking of the Human Body", IEEE Trans. on Pattern Anal. And Machine Intell. , Vol. 19, No. 7, pp. 780-785, 1997.
  • D. Koller, J. Weber, T. Huang, J. Malik, G. Ogasawara, B. Rao and S. Russell, "Towards Robust AutomaicTraffic Scene Analysis in Real-Time", Proc. ICPR'94, pp. 126-131, Nov. 1994.
  • B. P. L. Lo and S. A. Velastin, "Automatic Congestion Detection System for Underground Platforms", Proc. ISIMP 2001, pp. 158-161, May 2001.
  • R. Cucchiara, C. Grana, M. Piccardi and A. Prati, "Detecting Moving Objects, Ghosts and Shadows in Video Streams", IEEE Trans. On Pattern Anal. and Machine Intell. , Vol. 25, No. 10, pp. 1337-1442, 2003.
  • Stauffer C, Grimson W. E. L. , "Adaptive Background Mixture Models for Real-Time Tracking", Proceedings of conference on Computer Vision and Pattern Recognition (Cat. No PR00149). IEEE Computer Society Vol. 2, pp. 246-252, June 1999.
  • A. Elgammal, D. Harwood and L. S. Davis, "Non-Parametric Model for Background Subtraction", Proc. ECCV 2000, pp. 751-767, June 2000.
  • M. Piccardi, "Background subtraction techniques: a review", IEEE International conference on Systems, man and Cybernetics, pp. 3099-3104, 2004.
  • N. M. Oliver, B. Rosario and A. P. Pentland, "A Bayesian Computer Vision System for Modeling Human Interactions", IEEE Trans. on Pattern Anal. and Machine Intell. , Col. 25, No. 11, pp. 1499-1504, 2003.
  • Tsong-Yi Chen, Chao-Ho Chen, Da-Jinn Wang and Tsang-Jie Chen, "Real-Time Counting Method for a Crowd of Moving People", Sixth International Conferenceon Intelligent Information Hiding and Multimedia Signal Processing, pp. 643-646, 2010.
  • Raul Rojas, "The Kalman Filter", available on www. robocup. mi. fu-berlin. de/buch/kalman. pdf, pp. 1-7.
  • BerilSirmacek and Peter Reinartz, "KALMAN FILTER BASED FEATURE ANALYSIS FOR TRACKING PEOPLE FROMAIRBORNE IMAGES", Proc. ISPRS XXXVIII.
  • D. Comaniciu, V. Ramesh and P. Meer, "Kernel Based Object Tracking",IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 25, No. 5, 564-575, 2003.
  • D. Comaniciu and P. Meer, "Mean-shift Analysis and Applications", The Proc. of the 7th IEEE International Conference on Computer Vision, Vo. 2, pp. 1197-1203, 1999.
  • Tao Liu, Xiaping Cheng, "Improved Mean Shift Algorithm for Moving Object Tracking", 2nd International Conference on Computer Engineering and Technology(ICCET), Vol. 1, pp. 575-578, 2010.
  • Honglian Ma and Huchuan Lu Mingxiu Zhang, "A Real-time Effective System for Tracking Passing People Using a Single Camera", Proc. Of the 7th World Congress on Intelligent Control and Automation, June 25-27, 2008, Chongging, China, pp. 6173-6177.
  • A. J. Schofield, P. A. Mehta, T. J. Stonham, "A System for Counting People in Video images using Neural Networks to identify the Background scene", Journal of Pattern Recognition, Vol. 29, Issue no. 8, pp. 1421-1428, 1996.
  • C. H. Chen,Y. C. Chang, T. Y. Chen, D. J. Wang,"People Counting System for Getting In/Out of a Bus Based on Video Processing" IEEE Computer Society, Eighth International Conference on Intelligent Systems Design and Applications, pp. 565-569, 2008.
  • S. Hartono, T. Ji, T. Yap-Peng, "People Counting by Video Segmentation and Tracking", 9th international Conference on Control, Automation, Robotics and Vision, pp. 1-4, 2006.
  • Tsong-Yi Chen, Chao-Ho Chen, Da-Jinn Wang and Tsang-Jie Chen, "Real-Time Counting Method for a Crowd of Moving People", Sixth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, pp. 643-646, 2010.
  • J. W. Kim, K. S. Choi ,B. D. Choi, S. J. Ko, "Real-time Vision-based people counting system for security door", International Technical Conference on Circuits/Systems Computers and Communications, pp. 1416-1419, 2002.
  • Javier Barandiaran, Berta Murguia and Fernando Boto, "Real-Time People Counting Using Multiple Lines", IEEE Ninth International Workshop on Image Analysis for Multimedia Interactive Services, pp. 159-162, 2008.