Automatic Pedestrian Detection and Tracking for Real-Time Video Surveillance

  • Hee-Deok Yang
  • Bong-Kee Sin
  • Seong-Whan Lee
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2688)


This paper presents a method for tracking and identifying pedestrians from video images taken by a fixed camera at an entrance. A pedestrian may be totally or partially occluded in a scene for some period of time. The proposed approach uses the appearance model for the identification of pedestrians and the weighted temporal texture features. We compared the proposed method with other related methods using color and shape features, and analyzed the features’ stability. Experimental results with various real video data revealed that real time pedestrian tracking and recognition is possible with increased stability over 5–15% even under occasional occlusions in video surveillance applications.


Face Recognition Video Surveillance Appearance Model Pedestrian Detection Intensity Cluster 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. [1]
    Collins, T. et al.: A System for Video Surveillance and Monitoring:VSAM Finial Report. Technical report CMU-RI-TR-00-12, Robotics Institute, Carnegie Mellon University (May 2000)Google Scholar
  2. [2]
    Roh, H.-K., Lee, S.-W.: Multiple People Tracking Using an Appearance Model Based on Temporal Color. Proc. of 1st IEEE Int’l Workshop on Biologically Motivated Computer Vision, Seoul, Korea (May 2000) 369–378Google Scholar
  3. [3]
    Haritaoglu, I., Harwood, D., Davis, L. S.: W4: Who? When? Where? What? A Real Time System for Detecting and Tracking People. Proc. of Int’l Conf. on Face and Gesture Recognition, Nara, Japan (April 1998) 222–227Google Scholar
  4. [4]
    Darrell, T. et al.: Integrated Person Tracking Using Stereo, Color, and Pattern Detection. Proc. of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Santa Barbera, California, (1998) 601–608Google Scholar
  5. [5]
    Intille, S. S., Davis, J.W., Bobick, A. F.: Real-Time Closed-World Tracking. Proc. of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Puerto Rico, (Jun. 1997) 697–703Google Scholar
  6. [6]
    Baoxin, L., Chellappa, R.: A Generic Approach to Simultaneous Tracking and Verification in video. IEEE Trans. on Image Processing, Vol. 11, No. 5, (2002) 530–544CrossRefGoogle Scholar
  7. [7]
    Xi, D., Lee, S.-W.: Face Detection and Facial Feature Extraction Using Support Vector Machines. Proc. of 16th Int’l Conf. on Pattern Recognition, Quebec City, Canada, (August 2002) 209–212Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Hee-Deok Yang
    • 1
  • Bong-Kee Sin
    • 2
  • Seong-Whan Lee
    • 1
  1. 1.Center for Artificial Vision ResearchKorea UniversitySeoulKorea
  2. 2.Department of Computer MultimediaPukyong National UniversityPusanKorea

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