People Counting in Low Density Video Sequences

  • J. D. ValleJr.
  • L. E. S. Oliveira
  • A. L. Koerich
  • A. S. BrittoJr.
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4872)

Abstract

This paper presents a novel approach for automatic people counting in videos captured through a conventional closed-circuit television (CCTV) using computer vision techniques. The proposed approach consists of detecting and tracking moving objects in video scenes to further counting them when they enters into a virtual counting zone defined in the scene. One of the main problems of using conventional CCTV cameras is that they are usually not placed into a convenient position for counting and this may cause a lot of occlusions between persons when they are walking very close or in groups. To tackle this problem two strategies are investigated. The first one is based on two thresholds which are related to the average width and to the average area of a blob top zone, which represents a person head. By matching the width and the head region area of a current blob against these thresholds it is possible to estimate if the blob encloses one, two or three persons. The second strategy is based on a zoning scheme and extracts low level features from the top region of the blob, which is also related to a person head. Such feature vectors are used together with an instance-based classifier to estimate the number of persons enclosed by the blob. Experimental results on videos from two different databases have shown that the proposed approach is able to count the number of persons that pass through a counting zone with accuracy higher than 85%.

Keywords

People Counting Computer Vision Tracking 

References

  1. 1.
    Kim, J.-W., Choi, K.-S., Choi, B.-D., Ko, S.-J.: Real-time Vision-based People Counting System for the Security Door. In: Proc. of 2002 International Technical Conference On Circuits Systems Computers and Communications, Phuket (July 2002)Google Scholar
  2. 2.
    Snidaro, L., Micheloni, C., Chiavedale, C.: Video security for ambient intelligence. IEEE Transactions on Systems, Man and Cybernetics PART A 35(1), 133–144 (2005)CrossRefGoogle Scholar
  3. 3.
    Nakajima, C., Pontil, M., Heisele, B., Poggio, T.: People recognition in image sequences by supervised learning. In: MIT AI Memo (2000)Google Scholar
  4. 4.
    Gavrila, D.: Pedestrian detection from a moving vehicle. In: Proc. 6th European Conf. Computer Vision, Dublin, Ireland, vol. 2, pp. 37–49 (2000)Google Scholar
  5. 5.
    Giebel, J., Gavrila, D.M., Schnorr, C.: A bayesian framework for multi-cue 3d object tracking. In: Proc. 8th European Conf. Computer Vision, pp. 241–252. Prague, Czech Republic (2004)Google Scholar
  6. 6.
    Lin, S-F., Chen, J-Y., Chao, H-X.: Estimation of Number of People in Crowded Scenes Using Perspective Transformation. IEEE Trans. Systems, Man, and Cybernetics Part A 31(6), 645–654 (2001)CrossRefGoogle Scholar
  7. 7.
    Haritaoglu, I., Harwood, D., Davis, L.S.: W4: Real-Time Surveillance of People and Their Activities. IEEE Transactions on Pattern Analysis and Machine Intelligence, 809–830 (2000)Google Scholar
  8. 8.
    Hu, W., Tan, T., Wang, L., Maybank, S.J.: A Survey on Visual Surveillance of Object Motion and Behaviors. IEEE Trans. Systems, Man, Cybernetics, Part C, 334–352 (2004)Google Scholar
  9. 9.
    Lei, B., Xu, L.Q.: From Pixels to Objects and Trajectories: A generic real-time outdoor video surveillance system. In: IEE Intl Symp Imaging for Crime Detection and Prevention, pp. 117–122 (2005)Google Scholar
  10. 10.
    Latecki, L.J., Miezianko, R.: Object Tracking with Dynamic Template Update and Occlusion Detection. In: 18th Intl Conf on Pattern Recognition, pp. 556–560 (2006)Google Scholar
  11. 11.
    Lucas, B.D., Kanade, T.: An Iterative Image Registration Technique with an Application to Stereo Vision. In: 7th Intl Joint Conf Artificial Intelligence, pp. 674–679 (1981)Google Scholar
  12. 12.
    Aha, D.W., Kibler, D., Albert, M.K.: Instance-Based Learning Algorithms. Machine Learning 6, 37–66 (1991)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • J. D. ValleJr.
    • 1
  • L. E. S. Oliveira
    • 1
  • A. L. Koerich
    • 1
  • A. S. BrittoJr.
    • 1
  1. 1.Postgraduate Program in Computer Science (PPGIa), Pontifical Catholic University of Parana (PUCPR), R. Imaculada Conceição, 1155 Prado Velho, 80215-901, Curitiba, PRBrazil

Personalised recommendations