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short-paper

Dense Scene Multiple Object Tracking with Box-Plane Matching

Authors:

Feiyue HuangAuthors Info & Claims

MM '20: Proceedings of the 28th ACM International Conference on Multimedia

Pages 4615 - 4619

https://doi.org/10.1145/3394171.3416283

Published: 12 October 2020 Publication History

Abstract

Multiple Object Tracking (MOT) is an important task in computer vision. MOT is still challenging due to the occlusion problem, especially in dense scenes. Following the tracking-by-detection framework, we propose the Box-Plane Matching (BPM) method to improve the MOT performacne in dense scenes. First, we design the Layer-wise Aggregation Discriminative Model (LADM) to filter the noisy detections. Then, to associate remaining detections correctly, we introduce the Global Attention Feature Model (GAFM) to extract appearance feature and use it to calculate the appearance similarity between history tracklets and current detections. Finally, we propose the Box-Plane Matching strategy to achieve data association according to the motion similarity and appearance similarity between tracklets and detections. With the effectiveness of the three modules, our team achieves the 1st place on the Track-1 leaderboard in the ACM MM Grand Challenge HiEve 2020.

Supplementary Material

MP4 File (3394171.3416283.mp4)

Following the tracking-by-detection framework, we propose the Box-Plane Matching (BPM) method to improve the MOT performacne in dense scenes. First, we design the Layer-wise Aggregation Discriminative Model (LADM) to filter the noisy detections. Then, to associate remaining detections correctly, we introduce the Global Attention Feature Model (GAFM) to extract appearance feature and use it to calculate the appearance similarity between history tracklets and current detections. Finally, we propose the Box-Plane Matching strategy to achieve data association according to the motion similarity and appearance similarity between tracklets and detections. With the effectiveness of the three modules, our team achieves the 1st place on the Track-1 leaderboard in the ACM MM Grand Challenge HiEve 2020.

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Cited By

Li GJian YJian YYan YYan YWang HWang HCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)GLATrack: Global and Local Awareness for Open-Vocabulary Multiple Object TrackingProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681530(2457-2466)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681530
Shim KHwang JKo KKim C(2024)A Confidence-Aware Matching Strategy For Generalized Multi-Object Tracking2024 IEEE International Conference on Image Processing (ICIP)10.1109/ICIP51287.2024.10647729(4042-4048)Online publication date: 27-Oct-2024
https://doi.org/10.1109/ICIP51287.2024.10647729
Du CLin CJin RChai BYao YSu S(2024)Exploring the State-of-the-Art in Multi-Object Tracking: A Comprehensive Survey, Evaluation, Challenges, and Future DirectionsMultimedia Tools and Applications10.1007/s11042-023-17983-283:29(73151-73189)Online publication date: 9-Feb-2024
https://doi.org/10.1007/s11042-023-17983-2
Show More Cited By

Index Terms

Dense Scene Multiple Object Tracking with Box-Plane Matching
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Matching
        Object detection
        Object identification
        Tracking

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Information & Contributors

Information

Published In

cover image ACM Conferences

MM '20: Proceedings of the 28th ACM International Conference on Multimedia

October 2020

4889 pages

ISBN:9781450379885

DOI:10.1145/3394171

General Chairs:
Chang Wen Chen
Chinese University of Hong Kong, Shenzhen, China
,
Rita Cucchiara
UNIMORE, Italy
,
Xian-Sheng Hua
Alibaba Group, China
,
Program Chairs:
Guo-Jun Qi
Futurewei Technologies, USA
,
Elisa Ricci
UNITN & Fondazione Bruno Kessler, Italy
,
Zhengyou Zhang
Tencent, China
,
Roger Zimmermann
National University of Singapore, Singapore

Copyright © 2020 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

Sponsors

SIGMM: ACM Special Interest Group on Multimedia

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 October 2020

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MM '20

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SIGMM

MM '20: The 28th ACM International Conference on Multimedia

October 12 - 16, 2020

WA, Seattle, USA

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Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Cited By

Li GJian YJian YYan YYan YWang HWang HCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)GLATrack: Global and Local Awareness for Open-Vocabulary Multiple Object TrackingProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681530(2457-2466)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681530
Shim KHwang JKo KKim C(2024)A Confidence-Aware Matching Strategy For Generalized Multi-Object Tracking2024 IEEE International Conference on Image Processing (ICIP)10.1109/ICIP51287.2024.10647729(4042-4048)Online publication date: 27-Oct-2024
https://doi.org/10.1109/ICIP51287.2024.10647729
Du CLin CJin RChai BYao YSu S(2024)Exploring the State-of-the-Art in Multi-Object Tracking: A Comprehensive Survey, Evaluation, Challenges, and Future DirectionsMultimedia Tools and Applications10.1007/s11042-023-17983-283:29(73151-73189)Online publication date: 9-Feb-2024
https://doi.org/10.1007/s11042-023-17983-2
Yao MWang JPeng JChi MLiu CEl Saddik AMei TCucchiara RBertini MTobon Vallejo DAtrey PHossain M(2023)FOLT: Fast Multiple Object Tracking from UAV-captured Videos Based on Optical FlowProceedings of the 31st ACM International Conference on Multimedia10.1145/3581783.3611868(3375-3383)Online publication date: 26-Oct-2023
https://dl.acm.org/doi/10.1145/3581783.3611868
Tyagi BNigam SSingh R(2023)Human Detection and Tracking Based on YOLOv3 and DeepSORTCommunication and Intelligent Systems10.1007/978-981-99-2100-3_11(125-135)Online publication date: 25-Jul-2023
https://doi.org/10.1007/978-981-99-2100-3_11
Xu NLin WLu XWei YXu NLin WLu XWei Y(2023)HiEve Challenge on VOTVideo Object Tracking10.1007/978-3-031-44660-3_3(117-123)Online publication date: 5-Dec-2023
https://doi.org/10.1007/978-3-031-44660-3_3
He YWei XHong XKe WGong Y(2022)Identity-Quantity Harmonic Multi-Object TrackingIEEE Transactions on Image Processing10.1109/TIP.2022.315428631(2201-2215)Online publication date: 2022
https://doi.org/10.1109/TIP.2022.3154286
Wang GYang YZhong XYang Y(2022)An Improved FairMOT Method for Crowd Tracking and Counting in Subway Passages2021 6th International Conference on Intelligent Transportation Engineering (ICITE 2021)10.1007/978-981-19-2259-6_11(130-139)Online publication date: 1-Jun-2022
https://doi.org/10.1007/978-981-19-2259-6_11
Gan YHan RYin LFeng WWang SShen HZhuang YSmith JYang YCesar PMetze FPrabhakaran B(2021)Self-supervised Multi-view Multi-Human Association and TrackingProceedings of the 29th ACM International Conference on Multimedia10.1145/3474085.3475177(282-290)Online publication date: 17-Oct-2021
https://dl.acm.org/doi/10.1145/3474085.3475177
Liu YWang EXu SWang ZLiu MShu W(2021)Simple Online Unmanned Aerial Vehicle Tracking with Transformer2021 IEEE 20th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)10.1109/TrustCom53373.2021.00168(1235-1239)Online publication date: Oct-2021
https://doi.org/10.1109/TrustCom53373.2021.00168
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