Pedestrian Detection Using Global-Local Motion Patterns

  • Dhiraj Goel
  • Tsuhan Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4843)


We propose a novel learning strategy called Global-Local Motion Pattern Classification (GLMPC) to localize pedestrian-like motion patterns in videos. Instead of modeling such patterns as a single class that alone can lead to high intra-class variability, three meaningful partitions are considered - left, right and frontal motion. An AdaBoost classifier based on the most discriminative eigenflow weak classifiers is learnt for each of these subsets separately. Furthermore, a linear three-class SVM classifier is trained to estimate the global motion direction. To detect pedestrians in a given image sequence, the candidate optical flow sub-windows are tested by estimating the global motion direction followed by feeding to the matched AdaBoost classifier. The comparison with two baseline algorithms including the degenerate case of a single motion class shows an improvement of 37% in false positive rate.


Support Vector Machine Motion Pattern Global Motion Frontal Motion Linear Support Vector Machine 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Dhiraj Goel
    • 1
  • Tsuhan Chen
    • 1
  1. 1.Department of Electrical and Computer Engineering, Carnegie Mellon UniversityU.S.A.

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