Abstract. Multiple Object Tracking still remains a difficult problem due to appearance variations and occlusions of the targets or detection failures. Using sophisticated appearance models or performing data association over multiple frames are two common approaches that lead to gain in performances. Inspired by the success of sparse representations in Single Object Tracking, we propose to formulate the multi-frame data association step as an energy minimization problem, designing an energy that efficiently exploits sparse representations of all detections. Furthermore, we propose to use a structured sparsity-inducing norm to compute representations more suited to the tracking context. We perform extensive experiments to demonstrate the effectiveness of the proposed formulation, and evaluate our approach on two public authoritative benchmarks in order to compare it with several state-of-the-art methods.

To cite this version:Loïc Fagot-Bouquet, Romaric Audigier, Yoann Dhome, Frédéric Lerasle. Collaboration and spatialization for an efficient multi-person tracking via sparse representations. Advanced Video-and Signal-based Surveillance, 2015, Karlsruhe, Germany. hal-01763174 Collaboration and spatialization for an efficient multi-person tracking via sparse representations AbstractMulti-person tracking is a very difficult problem in Computer Vision as a tracking algorithm is facing several issues, such as appearance changes, targets' occlusions and similar appearances between people. In an online tracking-bydetection algorithm, robust and discriminative specific appearance models help handling these difficulties. As done in single object tracking, we use sparse representations to extract local features of the targets and study how these representations can be specifically employed for multi-person tracking. Experiments on several datasets show that considering spatial information is crucial in order to improve the tracking performances with local descriptions compared to holistic features. Using large collaborative representations also improve the tracking results by naturally discarding irrelevant local patches.

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