Rank-1 Tensor Approximation Based Multi-target Tracking
Abstract：Multi-target tracking is popularly formulated as the data association based optimization, while the multi-frame data association optimization is the NP hard multidimensional assignment problem intrinsically, and most current approaches are limited to the optimization solution with appropriate relaxations. This project focuses the research on multi-frame data association based visual tracking, and proposes a rank-1 tensor approximation based multi-target tracking framework. The main research contents include: 1) for the NP hard multidimensional assignment optimization, propose an effective rank-1 tensor approximation based iteration solution; 2) for the high-order trajectory affinity representation, propose the multi-way histogram comparison based similarity metrics and the entropy based measure; 3) for the interaction model, propose a non-maximum suppression constraint based motion context; 4) for the occlusion handling, propose a data-driven and scene understanding approach. The project stands on the academic frontier, and has both the theoretical and application significances. For one thing, the program focuses on the theoretical problem lying in the multiple target tracking, shows the close relationship between the multi-dimensional assignment problem and the rank-1 tensor approximation problem, and proposes an effective iteration solution for the NP hard optimization problem. For another, the program proposes an efficient target interaction model and effective occlusion handling method for the real tracking applications. Finally, it aims to provide the innovative theory and key techniques for multiple target tracking and some related research areas.
Keywords: multi-target tracking; visual surveillance; video analysis
Contact:
SHI Xinchu
E-mail: xcshi@nlpr.ia.ac.cn