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Person Re-identification using Sparse Representation with a Saliency-weighted Dictionary

  • Kim, Miri (Graduate School of Advanced Image Science, Multimedia, and Film, Chung-Ang University) ;
  • Jang, Jinbeum (Graduate School of Advanced Image Science, Multimedia, and Film, Chung-Ang University) ;
  • Paik, Joonki (Graduate School of Advanced Image Science, Multimedia, and Film, Chung-Ang University)
  • Received : 2017.06.16
  • Accepted : 2017.07.14
  • Published : 2017.08.30

Abstract

Intelligent video surveillance systems have been developed to monitor global areas and find specific target objects using a large-scale database. However, person re-identification presents some challenges, such as pose change and occlusions. To solve the problems, this paper presents an improved person re-identification method using sparse representation and saliency-based dictionary construction. The proposed method consists of three parts: i) feature description based on salient colors and textures for dictionary elements, ii) orthogonal atom selection using cosine similarity to deal with pose and viewpoint change, and iii) measurement of reconstruction error to rank the gallery corresponding a probe object. The proposed method provides good performance, since robust descriptors used as a dictionary atom are generated by weighting some salient features, and dictionary atoms are selected by reducing excessive redundancy causing low accuracy. Therefore, the proposed method can be applied in a large scale-database surveillance system to search for a specific object.

Acknowledgement

Grant : Intelligent Defense Boundary Surveillance Technology Using Collaborative Reinforced Learning of Embedded Edge Camera and Image Analysis

Supported by : IITP, Commercialization Promotion Agency for R&D Outcomes (COMPA)

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