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    CenterTrack3D: Improved CenterTrack More Suitable for Three-Dimensional Objects

    Source: Journal of Autonomous Vehicles and Systems:;2021:;volume( 001 ):;issue: 002::page 021004-1
    Author:
    Gu, Lipeng
    ,
    Sun, Shaoyuan
    ,
    Liu, Xunhua
    ,
    Li, Xiang
    DOI: 10.1115/1.4050863
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Compared with two-dimensional (2D) multi-object tracking (MOT) algorithms, three-dimensional (3D) multi-object tracking algorithms have more research significance and broad application prospects in the unmanned vehicles research field. Aiming at the problem of 3D multi-object detection and tracking, in this paper, the multi-object tracker CenterTrack, which focuses on 2D multi-object tracking task while ignoring object 3D information, is improved mainly from two aspects of detection and tracking, and the improved network is called CenterTrack3D. In terms of detection, CenterTrack3D uses the idea of attention mechanism to optimize the way that the previous-frame image and the heatmap of previous-frame tracklets are added to the current-frame image as input, and second convolutional layer of the hm output head is replaced by dynamic convolution layer, which further improves the ability to detect occluded objects. In terms of tracking, a cascaded data association algorithm based on 3D Kalman filter is proposed to make full use of the 3D information of objects in the image and increase the robustness of the 3D multi-object tracker. The experimental results show that, compared with the original CenterTrack and the existing 3D multi-object tracking methods, CenterTrack3D achieves 88.75% MOTA for cars and 59.40% MOTA for pedestrians and is very competitive on the KITTI tracking benchmark test set.
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      CenterTrack3D: Improved CenterTrack More Suitable for Three-Dimensional Objects

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    contributor authorGu, Lipeng
    contributor authorSun, Shaoyuan
    contributor authorLiu, Xunhua
    contributor authorLi, Xiang
    date accessioned2022-02-06T05:37:03Z
    date available2022-02-06T05:37:03Z
    date copyright5/4/2021 12:00:00 AM
    date issued2021
    identifier issn2690-702X
    identifier otherjavs_1_2_021004.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4278403
    description abstractCompared with two-dimensional (2D) multi-object tracking (MOT) algorithms, three-dimensional (3D) multi-object tracking algorithms have more research significance and broad application prospects in the unmanned vehicles research field. Aiming at the problem of 3D multi-object detection and tracking, in this paper, the multi-object tracker CenterTrack, which focuses on 2D multi-object tracking task while ignoring object 3D information, is improved mainly from two aspects of detection and tracking, and the improved network is called CenterTrack3D. In terms of detection, CenterTrack3D uses the idea of attention mechanism to optimize the way that the previous-frame image and the heatmap of previous-frame tracklets are added to the current-frame image as input, and second convolutional layer of the hm output head is replaced by dynamic convolution layer, which further improves the ability to detect occluded objects. In terms of tracking, a cascaded data association algorithm based on 3D Kalman filter is proposed to make full use of the 3D information of objects in the image and increase the robustness of the 3D multi-object tracker. The experimental results show that, compared with the original CenterTrack and the existing 3D multi-object tracking methods, CenterTrack3D achieves 88.75% MOTA for cars and 59.40% MOTA for pedestrians and is very competitive on the KITTI tracking benchmark test set.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleCenterTrack3D: Improved CenterTrack More Suitable for Three-Dimensional Objects
    typeJournal Paper
    journal volume1
    journal issue2
    journal titleJournal of Autonomous Vehicles and Systems
    identifier doi10.1115/1.4050863
    journal fristpage021004-1
    journal lastpage021004-8
    page8
    treeJournal of Autonomous Vehicles and Systems:;2021:;volume( 001 ):;issue: 002
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian