Hough Forest based Association Framework with Occlusion Handling for Multi-Target Tracking
Jun Xiang1, Nong Sang1, Jianhua Hou2, Rui Huang1, Changxin Gao1,*
1National Key Laboratory of Science and Technology on Multispectral Information Processing,
School of Automation, Huazhong University of Science and Technology, Wuhan, 430074, China
2Hubei Key Laboratory of Intelligent Wireless Communications,
South-central University for nationalities, Wuhan, 430074, China
Abstract
This paper presents a novel multi-target tracking approach consisting of two parts. The first part is the detection based association to form global tracks. Short yet reliable tracklets are firstly generated. By effectively combining appearance and motion information, a Hough forest learning framework is constructed to obtain a more discriminative affinity model and produce longer association between tracklets. In the second part, in order to connect isolate detections for trajectory consistency, we present an appearance similarity model based on mutual occlusion reasoning. A novel fusion feature template is designed to accurately compute the matching score between each isolated detection and target. Experimental results show significant improvements of our method when compared with several state-of-the-art methods.
Related Paper
Jun Xiang, Nong Sang, Jianhua Hou, Rui Huang, Changxin Gao*, "Hough Forest based Association Framework with Occlusion Handling for Multiple Targets Tracking," IEEE Signal Processing Letters, 23(2):257-261, 2016.(paper)