Iterative Multiple Hypothesis Tracking With Tracklet-Level Association

doi:10.1109/TCSVT.2018.2881123

	Iterative Multiple Hypothesis Tracking With Tracklet-Level Association
	Hao Sheng 1; Jiahui Chen 1; Yang Zhang 1; Wei Ke 2; Zhang Xiong 1; Jingyi Yu3,4
	2019-12-01
发表期刊	IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY (IF:8.3[JCR-2023],7.1[5-Year])
ISSN	1558-2205
卷号	29 期号:12
发表状态	已发表
DOI	10.1109/TCSVT.2018.2881123
摘要	This paper proposes a novel iterative maximum weighted independent set (MWIS) algorithm for multiple hypothesis tracking (MHT) in a tracking-by-detection framework. MHT converts the tracking problem into a series of MWIS problems across the tracking time. Previous works solve these NP-hard MWIS problems independently without the use of any prior information from each frame, and they ignore the relevance between adjacent frames. In this paper, we iteratively solve the MWIS problems by using the MWIS solution from the previous frame rather than solving the problem from scratch each time. First, we define five hypothesis categories and a hypothesis transfer model, which explicitly describes the hypothesis relationship between adjacent frames. We also propose a polynomial-time approximation algorithm for the MWIS problem in MHT. In addition to that, we present a confident short tracklet generation method and incorporate tracklet-level association into MHT, which further improves the computational efficiency. Our experiments on both MOT16 and MOT17 benchmarks show that our tracker outperforms all the previously published tracking algorithms on both MOT16 and MOT17 benchmarks. Finally, we demonstrate that the polynomial-time approximate tracker reaches nearly the same tracking performance.
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收录类别	SCI ; EI ; SCIE
来源库	IEEE
引用统计	正在获取...
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/29995
专题	信息科学与技术学院信息科学与技术学院_PI研究组_虞晶怡组
作者单位	1.School of Computer Science and the Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, Beijing, China 2.School of Public Administration, Macao Polytechnic Institute, Macao, China 3.School of Information Science and Technology, ShanghaiTech University, Shanghai, China 4.Department of Computer and Information Sciences, University of Delaware, Newark, USA
推荐引用方式 GB/T 7714	Hao Sheng,Jiahui Chen,Yang Zhang,et al. Iterative Multiple Hypothesis Tracking With Tracklet-Level Association[J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,2019,29(12).
APA	Hao Sheng,Jiahui Chen,Yang Zhang,Wei Ke,Zhang Xiong,&Jingyi Yu.(2019).Iterative Multiple Hypothesis Tracking With Tracklet-Level Association.IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,29(12).
MLA	Hao Sheng,et al."Iterative Multiple Hypothesis Tracking With Tracklet-Level Association".IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 29.12(2019).