CFP last date
22 April 2024
Call for Paper
May Edition
IJCA solicits high quality original research papers for the upcoming May edition of the journal. The last date of research paper submission is 22 April 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

A Robust Real Time People tracking and Counting incorporating shadow detection and removal

by J. L. Raheja, Sishir Kalita, Pallab Jyoti Dutta, Solanki Lovendra
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 46 - Number 4
Year of Publication: 2012
Authors: J. L. Raheja, Sishir Kalita, Pallab Jyoti Dutta, Solanki Lovendra
10.5120/6900-9256

J. L. Raheja, Sishir Kalita, Pallab Jyoti Dutta, Solanki Lovendra . A Robust Real Time People tracking and Counting incorporating shadow detection and removal. International Journal of Computer Applications. 46, 4 ( May 2012), 51-58. DOI=10.5120/6900-9256

@article{ 10.5120/6900-9256,
author = { J. L. Raheja, Sishir Kalita, Pallab Jyoti Dutta, Solanki Lovendra },
title = { A Robust Real Time People tracking and Counting incorporating shadow detection and removal },
journal = { International Journal of Computer Applications },
issue_date = { May 2012 },
volume = { 46 },
number = { 4 },
month = { May },
year = { 2012 },
issn = { 0975-8887 },
pages = { 51-58 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume46/number4/6900-9256/ },
doi = { 10.5120/6900-9256 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:38:55.764428+05:30
%A J. L. Raheja
%A Sishir Kalita
%A Pallab Jyoti Dutta
%A Solanki Lovendra
%T A Robust Real Time People tracking and Counting incorporating shadow detection and removal
%J International Journal of Computer Applications
%@ 0975-8887
%V 46
%N 4
%P 51-58
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Video processing serves as a hidden treasure, rather a boon in disguise to surveillance system. The counting of people passing through a surveillance area is an important issue of this domain. It always relies on the process of background subtraction. The estimation of dynamic background model and the shadow removal are two main challenges of background subtraction. In this paper, a bi-directional people counting algorithm is proposed. To develop a robust counting system, Gaussian mixture model (GMM) is used to describe the background scene. But this algorithm does not provide a way to classify the shadows from the moving foreground objects. To achieve better performance, background model is upgraded by combining a Chromatic color model. This provides better improvement in moving objects detection by eliminating the shadows from foreground. A multi-class feature based tracking algorithm is applied for multiple tracking to handle occlusion problem. To improve the counting of individual in both directions a scheme is proposed, which is developed by a multi-level reverse tracking procedure. This proposed counting system seems to provide higher accuracy and better performance even under crowded situation and changing environmental situations. Experimental results show that high accuracy of bi-directional counting can be achieved if the density of the people access is low.

References
  1. Honglian, M. , Huchuan, L. Mingxiu, Z. , "A Real-time Effective System for Tracking Passing People Using a Single Camera", Proceedings of the 7thWorld Congress on Intelligent Control and Automation, Chongqing, China, pp. 6173-6177, 2008.
  2. Wren, C. , Azarhayejani, A. , Darrell, T. , Pentland, A. P. , "Pfinder: real-time tracking of the human body", IEEE Trans. on Pattern Analysis. And Machine Intelligence, vol. 19, No. 7, pp. 780-785, 1997.
  3. Lo, B. P. L. Velastin, S. A. , "Automatic congestion detection system for underground platforms", Proc. Of Int. Symposium on Intelligent Multimedia, Video and Speech Processing (ISIMP2001), pp. 158-161, 2001.
  4. Stauffer, C. , Grimson, W. E. L. , 1, "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. 246-25, 1999.
  5. Elgammal, A. , Hanvood, D. , Davis, L. S. , "Nonparametric model for background subtraction", Proc. Of European Conf. on Computer Vision (ECCV 2000), pp. 751-767, 2000.
  6. Koller, D. , Weber, J. , Huang, T. , Malik, J. , Ogasawara, G. , Rao, B. , and Russell, S. , "Towards Robust Automatic Traffic Scene Analysis in Real-time", Proc. Of Int. Conf. on Pattern Recognition (ICPR'94), pp. 126-131, 1994.
  7. Horprasert, T. , Harwood D. , Davis, L. S. , "A Statistical Approach for Real-Time Robust Background Subtraction and Shadow Detection", in IEEE FRAME-RATE WORKSHOP of International Conf. on Computer Vision (ICCV'99), 1999.
  8. Schofield, A. J. , Mehta, P. A. , Stonham, T. J. , "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.
  9. Terada,K. , and Kurokawa, N. , "A Method of Counting the Passing People by Using the Method of the Template Matching", IAPR Workshop on Machine Vision Applications, Makuhan, Chiba. Japan, pp. 498-501, 1998.
  10. 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,.
  11. Hartono, S. , Ji T. , Yap-Peng, T. , "People Counting by Video Segmentation and Tracking", 9th international Conference on Control, Automation, Robotics and Vision, pp. 1-4, 2006.
  12. Tsong-Yi, C. , Chao-Ho, C. , Da-Jinn, W. , Tsang-Jie, C. , "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.
  13. Kim, J. W. , Choi, K. S. , Choi, B. D. , Ko, S. J. , "Real-time Vision-based people counting system for security door", International Technical Conference on Circuits/Systems Computers and Communications, pp. 1416-1419, 2002.
  14. Javier, B. , Berta, M. , Fernando, B. , "Real-Time People Counting Using Multiple Lines", IEEE Computer Society, Ninth International Workshop on Image Analysis for Multimedia Interactive Services, pp. 159-162, 2008.
  15. Huazhong, Xu, Pei, L. , Lei, M. , "A People Counting System based on Head-shoulder Detection and Tracking in Surveillance Video", International Conference on Computer Design and Applications, pp. VI-394 –VI-398, 2010.
  16. Chunhui, Z. , Quan, P. , Stan, Z. L. , "Real Time People Tracking and Counting in Visual Surveillance", Proceedings of the 6th World Congress on Intelligent Control and Automation, Dalian, pp. 9722-9724, June 21 – 23, 2006.
  17. Bescos, J. , Menendez, J. M. , Garcia, N. , "DCT Based Segmentation Applied to a Scalable Zenithal People Counter", IEEE International Conference on Image Processing (ICIP), vol. 3, pp. 1005-1008, 2003.
  18. Enwei, Z. , Feng, C. , "A Fast and Robust People Counting Method in Video Surveillance", International Conference on Computational Intelligence and Security pp. 339-343, 2007.
  19. Dar-Shyang, L. , Jonathan, J. H. , Berna, E. , "A Bayesian framework for Gaussian mixture background modeling", International Conference on Image Processing, pp. 973-976, 2003.
  20. Fredrik, K. , Peter, N. , and Viktor, O. , "Background Segmentation beyond RGB", in Proceedings of Asian Conf. on Computer Vision (ACCV 2006), Hyderabad, India, pp. 602-612, 2006.
  21. Alessandro, L. , Cosimo, D. , Francesco B. , "A Shadow Elimination Approach in Video-Surveillance Context", Pattern Recognition Letters 27, pp. 345-355. 2006.
  22. Gonzalez, R. C. , and Woods, R. E. , "Digital Image Processing", Prentice Hall, 2nd Edition 2007.
  23. Cazan, I. , "Kalman Filters," available at, www. colby. edu/math/program/cazan-honors. pdf, pp. 1–2, 2011.
Index Terms

Computer Science
Information Sciences

Keywords

Video Surveillance People Counting Gaussian Mixture Model Merge-split Morphological Processing

Powered by PhDFocusTM