Multi-view Pedestrian Recognition Using Shared Dictionary Learning with Group Sparsity

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7064)


Pedestrian tracking in multi-camera is an important task in intelligent visual surveillance system, but it suffers from the problem of large appearance variations of the same person under different cameras. Inspired by the success of existing view transformation model in multi-view gait recognition, we present a novel view transformation model based approach named shared dictionary learning with group sparsity to address the problem. It projects the pedestrian appearance feature descriptor in probe view into the gallery one before feature descriptors matching. In this case, L 1, ∞  regularization over the latent embedding ensure the lower reconstruction error and more stable feature descriptors generation, comparing with the existing Singular Value Decomposition. Although the overall optimization function is not global convex, the Nesterovs optimal gradient scheme ensure the efficiency and reliability. Experiments on VIPeR dataset show that our approach reaches the state-of-the-art performance.


multiview learning dimension reduction stochastic neighbor embedding image retrieval 


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© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.National Laboratory of Pattern RecognitionInstitute of Automation, Chinese Academy of SciencesBeijingChina
  2. 2.Chinese University of Hong KongHong Kong, China
  3. 3.Centre for Quantum Computation and Intelligent Systems, Faculty of Engineering and Information TechnologyUniversity of TechnologySydneyAustralia

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