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ONLINE MULTI-PERSON TRACKING BASED ON GLOBAL SPARSE COLLABORATIVE REPRESENTATIONS

Loïc Fagot-Bouquet 1 Romaric Audigier 1 Yoann Dhome 1 Frédéric Lerasle 2
1 LVIC - Laboratoire Vision et Ingénierie des Contenus
DIASI - Département Intelligence Ambiante et Systèmes Interactifs : DRT/LIST/DIASI
2 LAAS-RAP - Équipe Robotique, Action et Perception
LAAS - Laboratoire d'analyse et d'architecture des systèmes
Abstract : Multi-person tracking is still a challenging problem due to recurrent occlusion, pose variation and similar appearances between people. Inspired by the success of sparse representations in single object tracking and face recognition, we propose in this paper an online tracking by detection framework based on collaborative sparse representations. We argue that collaborative representations can better differentiate people compared to target-specific models and therefore help to produce a more robust tracking system. We also show that despite the size of the dictionaries involved, these representations can be efficiently computed with large-scale optimization techniques to get a near real-time algorithm. Experiments show that the proposed approach compares well to other recent online tracking systems on various datasets.
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https://hal.laas.fr/hal-01763151
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Submitted on : Tuesday, April 10, 2018 - 6:07:48 PM
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  • HAL Id : hal-01763151, version 1

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Loïc Fagot-Bouquet, Romaric Audigier, Yoann Dhome, Frédéric Lerasle. ONLINE MULTI-PERSON TRACKING BASED ON GLOBAL SPARSE COLLABORATIVE REPRESENTATIONS. International Conference on Image Processing, Sep 2015, Québec, Canada. ⟨hal-01763151⟩

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