A Directed Sparse Graphical Model for Multi-target Tracking

Abstract

We propose a Directed Sparse Graphical Model (DSGM) for multi-target tracking. In the category of global optimization for multi-target tracking, traditional approaches have two main drawbacks. First, a cost function is defined in terms of the linear combination of the spatial and appearance constraints of the targets which results a highly non-convex function. And second, a very dense graph is constructed to capture the global attribute of the targets. In such a graph, It is impossible to find reliable tracks in polynomial time unless some relaxation and heuristics are used. To address these limitations, we proposed DSGM which finds a set of reliable tracks for the targets without any heuristics or relaxation and keeps the computational complexity very low through the design of the graph. Irrespective of traditional approaches where spatial and appearance constraints are added up linearly with a given weight factor, we incorporated these constraints in a cascaded fashion. First, we exploited a Hidden Markov Model (HMM) for the spatial constraints of the target and obtain most probable locations of the targets in a segment of video. Afterwards, a deep feature based appearance model is used to generate the sparse graph. The track for each target is found through dynamic programming. Experiments are performed on 3 challenging sports datasets (football, basketball and sprint) and promising results are achieved.