End-to-End Joint Multi-Object Detection and Tracking for Intelligent Transportation Systems

查看全文 作　　者：Qing [1]Xu;Xuewu [2]Lin;Mengchi [1]Cai;Yu‑ang [3]Guo;Chuang [1]Zhang;Kai [4]Li;Keqiang [1]Li;Jianqiang [1]Wang;Dongpu [5]Cao

机构地区：[1]School of Vehicle and Mobility,Tsinghua University,Beijing 100084,China;[2]Horizon Information Technology Co.,Ltd.,Beijing 100096,China;[3]School of Transportation Science and Engineering,Beihang University,Beijing 100191,China;[4]Dongfeng USharing Technology Co.,Ltd.,Wuhan 430000,China;[5]Department of Mechanical and Mechatronics Engineering,University of Waterloo,Waterloo,ON N2L 3G1,Canada

出　　处：《Chinese Journal of Mechanical Engineering》索引2023年第36卷第5期,共11页

基　　金：Supported by National Key Research and Development Program of China(Grant No.2021YFB1600402);National Natural Science Foundation of China(Grant No.52072212);Dongfeng USharing Technology Co.,Ltd.,China Intelli‑gent and Connected Vehicles(Beijing)Research Institute Co.,Ltd.;“Shuimu Tsinghua Scholarship”of Tsinghua University of China. 摘　　要：Environment perception is one of the most critical technology of intelligent transportation systems(ITS).Motion interaction between multiple vehicles in ITS makes it important to perform multi-object tracking(MOT).However,most existing MOT algorithms follow the tracking-by-detection framework,which separates detection and tracking into two independent segments and limit the global efciency.Recently,a few algorithms have combined feature extraction into one network;however,the tracking portion continues to rely on data association,and requires com‑plex post-processing for life cycle management.Those methods do not combine detection and tracking efciently.This paper presents a novel network to realize joint multi-object detection and tracking in an end-to-end manner for ITS,named as global correlation network(GCNet).Unlike most object detection methods,GCNet introduces a global correlation layer for regression of absolute size and coordinates of bounding boxes,instead of ofsetting predictions.The pipeline of detection and tracking in GCNet is conceptually simple,and does not require compli‑cated tracking strategies such as non-maximum suppression and data association.GCNet was evaluated on a multivehicle tracking dataset,UA-DETRAC,demonstrating promising performance compared to state-of-the-art detectors and trackers. 关键词：Intelligent transportation systems Joint detection and tracking Global correlation network End-to-end tracking

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End-to-End Joint Multi-Object Detection and Tracking for Intelligent Transportation Systems

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