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Fast Online Multi-Pedestrian Tracking via Integrating Motion Model and Deep Appearance Model
He M(何淼)1,2,3,4,5; Luo HB(罗海波)1,2,4,5; Hui B(惠斌)1,2,4,5; Chang Z(常铮)1,2,4,5
Department光电信息技术研究室
Source PublicationIEEE Access
ISSN2169-3536
2019
Volume7Pages:89475-89486
Indexed BySCI ; EI
EI Accession number20193007236678
WOS IDWOS:000476816700008
Contribution Rank1
KeywordOnline, pedestrian detection multi-object tracking re-identifying Kalman lter data association
AbstractIn 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.
Language英语
WOS SubjectComputer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS KeywordMULTITARGET TRACKING ; TARGET
WOS Research AreaComputer Science ; Engineering ; Telecommunications
Citation statistics
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/25312
Collection光电信息技术研究室
Corresponding AuthorHe M(何淼)
Affiliation1.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
2.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China
3.Research Institute, University of Chinese Academy of Sciences, Beijing 100049, China
4.Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Science, Shenyang 110016, China
5.Key Lab of Image Understanding and Computer Vision, Shenyang 110016, China
Recommended Citation
GB/T 7714
He M,Luo HB,Hui B,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,Luo HB,Hui B,&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.
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