Depth Structure Association for RGB-D Multi-target Tracking
Shan Gao, Zhenjun Han, David Doermann, Jianbin Jiao · 2014
Multi-target tracking in outdoor scenes plays an important role in many computer vision applications. Most previous work on visual information based multi-target tracking does not incorporate depth information and the absence of depth information often leads to mismatching or tracking failures. In this paper, we propose a Depth Structure Association (DSA) approach for RGB-D data based multi-target tracking. DSA encodes depth information in a chain structure, the structure is used by DSA together with appearance and motion information to address object occlusion issues in outdoor scenes. Additionally, the use of DSA has the advantages of regulating a much smaller solution space, greatly reducing the computational complexity. Experimental results on three datasets demonstrate that our DSA approach can significantly reduce object mismatch and tracking failure for long term occlusions.