Title

Gmmcp Tracker: Globally Optimal Generalized Maximum Multi Clique Problem For Multiple Object Tracking

Abstract

Data association is the backbone to many multiple object tracking (MOT) methods. In this paper we formulate data association as a Generalized Maximum Multi Clique problem (GMMCP). We show that this is the ideal case of modeling tracking in real world scenario where all the pairwise relationships between targets in a batch of frames are taken into account. Previous works assume simplified version of our tracker either in problem formulation or problem optimization. However, we propose a solution using GMMCP where no simplification is assumed in either steps. We show that the NP hard problem of GMMCP can be formulated through Binary-Integer Program where for small and medium size MOT problems the solution can be found efficiently. We further propose a speed-up method, employing Aggregated Dummy Nodes for modeling occlusion and miss-detection, which reduces the size of the input graph without using any heuristics. We show that, using the speedup method, our tracker lends itself to real-time implementation which is plausible in many applications. We evaluated our tracker on six challenging sequences of Town Center, TUD-Crossing, TUD-Stadtmitte, Parking-lot 1, Parking-lot 2 and Parking-lot pizza and show favorable improvement against state of art.

Publication Date

10-14-2015

Publication Title

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volume

07-12-June-2015

Number of Pages

4091-4099

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/CVPR.2015.7299036

Socpus ID

84956695994 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84956695994

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