Fast Online Multi-Pedestrian Tracking via Integrating Motion Model and Deep Appearance Model

CORC > 沈阳自动化研究所 > 中国科学院沈阳自动化研究所 > 光电信息技术研究室

	Fast Online Multi-Pedestrian Tracking via Integrating Motion Model and Deep Appearance Model
	He M(何淼)1,2,3,4,5; Hui B(惠斌)1,2,4,5 ; Luo HB(罗海波)1,2,4,5 ; Chang Z(常铮)1,2,4,5
刊名	IEEE Access
	2019
卷号	7 页码:89475-89486
关键词	Online, pedestrian detection multi-object tracking re-identifying Kalman lter data association
ISSN号	2169-3536
产权排序	1
英文摘要	In recent years, multi-object tracking has attracted more and more attention, both in academia and engineering, but most of the recent works do not pay attention to the speed of the algorithm and only pursue the accuracy. In this paper, we propose an online multi-pedestrian tracking algorithm, taking into account both the accuracy and the speed. First, the motion models of the targets are established by the Kalman filter. At the same time, the appearance models of the targets are extracted by the convolutional neural network. Moreover, a data association algorithm is proposed, which integrates the motion information, including scale, intersection-over-union, and distance, and the appearance information, including the current appearance model and the long-Term appearance model. With the data association algorithm, the matching between detections and tracklets is realized, and the goal of tracking by detection is achieved. We compare the proposed algorithm with other algorithms on the MOT15 benchmark and the MOT16 benchmark. The experiment results show that the algorithm has high accuracy and good real-Time performance.
WOS关键词	MULTITARGET TRACKING ; TARGET
WOS研究方向	Computer Science ; Engineering ; Telecommunications
语种	英语
WOS记录号	WOS:000476816700008
内容类型	期刊论文
源URL	[http://ir.sia.cn/handle/173321/25312]
专题	沈阳自动化研究所_光电信息技术研究室
通讯作者	He M(何淼)
作者单位	1.Key Lab of Image Understanding and Computer Vision, Shenyang 110016, China 2.Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Science, Shenyang 110016, China 3.Research Institute, University of Chinese Academy of Sciences, Beijing 100049, China 4.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China 5.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
推荐引用方式 GB/T 7714	He M,Hui B,Luo HB,et al. Fast Online Multi-Pedestrian Tracking via Integrating Motion Model and Deep Appearance Model[J]. IEEE Access,2019,7:89475-89486.
APA	He M,Hui B,Luo HB,&Chang Z.(2019).Fast Online Multi-Pedestrian Tracking via Integrating Motion Model and Deep Appearance Model.IEEE Access,7,89475-89486.
MLA	He M,et al."Fast Online Multi-Pedestrian Tracking via Integrating Motion Model and Deep Appearance Model".IEEE Access 7(2019):89475-89486.