Title | ||
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People re-identification by classification of silhouettes based on sparse representation |
Abstract | ||
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The research presented in this paper consists in developing an automatic system for people re-identification across multiple cameras with non-overlapping fields of view. We first propose a robust algorithm for silhouette extraction which is based on an adaptive spatio-colorimetric background and foreground model coupled with a dynamic decision framework. Such a method is able to deal with the difficult conditions of outdoor environments where lighting is not stable and distracting motions are very numerous. A robust classification procedure, which exploits the discriminative nature of sparse representation, is then presented to perform people re-identification task. The global system is tested on two real data sets recorded in very difficult environments. The experimental results show that the proposed system leads to very satisfactory results compared to other approaches of the literature. |
Year | DOI | Venue |
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2010 | 10.1109/IPTA.2010.5586809 | IPTA |
Keywords | Field | DocType |
sparse representation,motions distraction,silhouette extraction,people reidentification,sparse matrices,camera,spatiocolorimetric foreground,spatiocolorimetric background,silhouette classification,robust algorithm,feature extraction,cameras,discriminative nature,dynamic decision framework,people detection,robust classification procedure,people re-identification,surveillance system,video surveillance,data set,robustness,field of view,pixel | Computer vision,Data set,Pattern recognition,Computer science,Silhouette,Sparse approximation,Robustness (computer science),Feature extraction,Pixel,Artificial intelligence,Discriminative model,Sparse matrix | Conference |
ISSN | ISBN | Citations |
2154-5111 | 978-1-4244-7247-5 | 9 |
PageRank | References | Authors |
0.53 | 9 | 3 |
Name | Order | Citations | PageRank |
---|---|---|---|
Dung Nghi Truong Cong | 1 | 53 | 4.14 |
Catherine Achard | 2 | 158 | 19.60 |
Louahdi Khoudour | 3 | 117 | 14.20 |