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Communication Dans Un Congrès Année : 2013

Multi-shot SURF-based person re-identification via sparse representation

Résumé

We present in this paper a multi-shot human re-identification system from video sequences based on SURF matching. Our contribution is about the matching step which is crucial. In this context, we propose a new method of SURF matching via sparse representation. Each SURF Interest Point in the test sequence is represented by a sparse representation of SURFs points in the reference dataset. For efficiency purposes, a dynamic dictionary is selected for each SURF from this dataset through KD-Tree Neighborhood search. Then a majority vote rule is applied to classify the test sequence. This approach is evaluated on two public datasets : PRID-2011 and CAVIAR4REID. The experimental results show that our approach compares favorably with and outperforms current state-of-the-art on the two datasets by 1% to 7%.
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Dates et versions

hal-01262131 , version 1 (26-01-2016)

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Citer

Mohamed Ibn Khedher, Mounim El Yacoubi, Bernadette Dorizzi. Multi-shot SURF-based person re-identification via sparse representation. AVSS 2013 : 10th IEEE International Conference on Advanced Video and Signal-Based Surveillance, Aug 2013, Kraków, Poland. pp.159 - 164, ⟨10.1109/AVSS.2013.6636633⟩. ⟨hal-01262131⟩
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