Collaboration and spatialization for an efficient multi-person tracking via sparse representations

Abstract

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.

Improving Multi-frame Data Association with Sparse Representations for Robust Near-online Multi-object Tracking

“…However, as many specific and independent models as the number of targets are necessary. In contrast, in [3,27], a single dictionary is shared by all targets and collaborative representations are used to better discriminate them. All these MOT approaches are using a twoframe data association in an online fashion and thus cannot reconsider wrong associations when further information comes and contradicts them.…”

“…Our approach is inspired by [3,27,28], but instead of relying on sparse representations induced by the standard l 1 norm, we design a sparsity-inducing norm, based on a weighted l ∞,1 norm, more suited for a multi-frame data association problem.…”

“…Each candidate for the new target location is then represented by a sparse linear combination of the dictionary elements, the best reconstruction error being used as the selection criterion. However, only a few recent methods have considered extending these models for online MOT systems [3,27,28].…”