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Techniques d'optimisation pour la détection et ré-identification de personnes dans un réseau de caméras

Abstract : This thesis deals with people detection and re-identification in an environment instrumented by a network of disjoint-field cameras. It stands at the confluence of the Operational Research and Computer Vision communities as combinatorial optimization techniques are used to formalize new computer vision methods. In this context, a people visual detector, based on mixed-integer programming, is first propose that simultaneously take computation time and detection performances into account. This detector is evaluated and compared to the best detectors of the literature. These experiments, conducted on two public databases, clearly demonstrate the interest of our detector in terms of processing time with detection performance guarantee. The second part of the thesis deals with people re-identification. Our novel approach, called D-NCR (Directed Network Consistent Re-identification), explicitly takes minimum transit times in the camera network into account, as well as the network topology, in order to improve the re-identification performance. This problem is similar to the determination of particular maximum-profitable independent paths in an oriented graph. A mixed-integer program is proposed to model and solve this problem. The experiments made on a public dataset sound promising and tend to prove the potential of the approach.
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Submitted on : Wednesday, December 4, 2019 - 11:43:12 AM
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  • HAL Id : tel-02079969, version 2


Francisco Rodolfo Barbosa-Anda. Techniques d'optimisation pour la détection et ré-identification de personnes dans un réseau de caméras. Systèmes et contrôle [cs.SY]. Université Paul Sabatier - Toulouse III, 2018. Français. ⟨NNT : 2018TOU30314⟩. ⟨tel-02079969v2⟩



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