Person Re-identification using Sparse Representation with a Saliency-weighted Dictionary
- Authors
- Kim, Miri; Jang, Jinbeum; Paik, Joonki
- Issue Date
- Aug-2017
- Publisher
- 대한전자공학회
- Keywords
- Re-identification; Sparse representation; Saliency; Occlusion; Surveillance
- Citation
- IEIE Transactions on Smart Processing & Computing, v.6, no.4, pp 262 - 268
- Pages
- 7
- Journal Title
- IEIE Transactions on Smart Processing & Computing
- Volume
- 6
- Number
- 4
- Start Page
- 262
- End Page
- 268
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/5542
- DOI
- 10.5573/IEIESPC.2017.6.4.262
- ISSN
- 2287-5255
- 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.
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Collections - Graduate School of Advanced Imaging Sciences, Multimedia and Film > Department of Imaging Science and Arts > 1. Journal Articles
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