Unsupervised data association for metric learning in the context of multi-shot person re-identification

Furqan Khan 1 Francois Bremond 1
1 STARS - Spatio-Temporal Activity Recognition Systems
CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : Appearance based person re-identification is a challenging task, specially due to difficulty in capturing high intra-person appearance variance across cameras when inter-person similarity is also high. Metric learning is often used to address deficiency of low-level features by learning view specific re-identification models. The models are often acquired using a supervised algorithm. This is not practical for real-world surveillance systems because annotation effort is view dependent. In this paper, we propose a strategy to automatically generate labels for person tracks to learn similarity metric for multi-shot person re-identification task. We demonstrate on multiple challenging datasets that the proposed labeling strategy significantly improves performance of two baseline methods and the extent of improvement is comparable to that of manual annotations in the context of KISSME algorithm.
Type de document :
Communication dans un congrès
Advance Video and Signal based Surveillance, Aug 2016, Colorado Springs, United States. 〈10.1109/AVSS.2016.7738058〉
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https://hal.inria.fr/hal-01400147
Contributeur : Furqan Khan <>
Soumis le : lundi 21 novembre 2016 - 14:52:11
Dernière modification le : jeudi 11 janvier 2018 - 16:48:47

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Furqan Khan, Francois Bremond. Unsupervised data association for metric learning in the context of multi-shot person re-identification. Advance Video and Signal based Surveillance, Aug 2016, Colorado Springs, United States. 〈10.1109/AVSS.2016.7738058〉. 〈hal-01400147〉

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