Action-Based Pedestrian Identification via Hierarchical Matching Pursuit and Order Preserving Sparse Coding

Abstract

Pedestrian identification is a very important topic in the area of intelligent surveillance and public safety, where the near front face images of pedestrian can hardly be obtained due to high installation angle of camera, long-distance location and extreme light variations. This paper presents a new action-based pedestrian identification algorithm, which adopts hierarchical matching pursuit (HMP) to extract features and order preserving sparse coding (OPSC) to do classification. Two-layer HMP features are extracted from foreground frame image patches by sparse coding, max pooling and normalization, which preserve both local and global information. OPSC is taken as classifier to take full advantage of the spatial structure information, which is different from traditional temporal OPSC algorithm. The spatiotemporal order preserving sparse coding-based classification is also investigated. The effectiveness of the proposed method is verified on public data sets, and the experimental results show the superiority of our method.