Multi-object tracking using hierarchical data association based on generalized correlation clustering graphs

作者：Qi, Mei Bin; Yue, Zhou Long^*; Shu, Kun; Jiang, Jian Guo

来源：Acta Automatica Sinica, 2017, 43(1): 152-160.

DOI：10.16383/j.aas.2017.c150519

• Generalized correlation
• Hierarchical data
• Multi-object tracking
• Occlusion handing
• Tracking by detections

Tracking by detection based on data association of detections is a main research direction in the field of multi-object tracking. The majority of current methods, such as bipartite matching, solve the data association problem between adjacent frames. We propose an approach to solve data association for one object whose all detections are in a sliding temporal window at a time. The task of multi-object tracking can be considered as a graph partitioning problem that takes the form of a generalized correlation clustering problem (GCCP). The multi-object tracking problem can be divided into two phases using hierarchical data association. Firstly, adaptive length tracklets can be obtained from all detections in a sliding temporal window by using the GCCP. The length of tracklets is not restricted to the window width. Secondly, we treat tracklets as detections, using the similar method described in first phase to acquire trajectories. Experiments show that the proposed method can handle occlusion and ID-switch effectively, which makes significant improvement in multi-object tracking on the public datasets. Our multiple object tracking accuracy (MOTA) is higher than that of the-state-of-the-art.

• 出版日期2017
• 单位合肥工业大学