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Connected Component Model for Multi-Object Tracking

delete2016-08-01
delete125
PRE
AI
Z
Zhenyu He
X
Xin Li
X
Xinge You *
D
Dacheng Tao *
Y
Yuan Yan Tang
DOI:10.1109/TIP.2016.2570553delete
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Abstract

Abstract

En 中文
In multi-object tracking, it is critical to explore the data associations by exploiting the temporal information from a sequence of frames rather than the information from the adjacent two frames. Since straightforwardly obtaining data associations from multi-frames is an NP-hard multi-dimensional assignment (MDA) problem, most existing methods solve this MDA problem by either developing complicated approximate algorithms, or simplifying MDA as a 2D assignment problem based upon the information extracted only from adjacent frames. In this paper, we show that the relation between associations of two observations is the equivalence relation in the data association problem, based on the spatial-temporal constraint that the trajectories of different objects must be disjoint. Therefore, the MDA problem can be equivalently divided into independent subproblems by equivalence partitioning. In contrast to existing works for solving the MDA problem, we develop a connected component model (CCM) by exploiting the constraints of the data association and the equivalence relation on the constraints. Based upon CCM, we can efficiently obtain the global solution of the MDA problem for multi-object tracking by optimizing a sequence of independent data association subproblems. Experiments on challenging public data sets demonstrate that our algorithm outperforms the state-of-the-art approaches.
Keywords:
Multi-object tracking
connected component model (CCM)
equivalence relation
data association
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Journal

IEEE Transactions on Image Processing cover
IEEE Transactions on Image Processing
IF:
13.7
Papers:
1.0W
Citations:
8.4W

Organization

H
harbin institute of technology
Scholars:
8.0W
Papers: 6.6W
Citations: 66
U
university of technology sydney
Scholars:
1.6W
Papers: 2.0W
Citations: 25
U
University of Macau
Scholars:
1.1W
Papers: 1.3W
Citations: 2.0W
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