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On-Road Pedestrian Tracking Across Multiple Driving Recorders

Authors:

Jenq-Neng HwangAuthors Info & Claims

IEEE Transactions on Multimedia, Volume 17, Issue 9

Pages 1429 - 1438

https://doi.org/10.1109/TMM.2015.2455418

Published: 01 September 2015 Publication History

Abstract

In this paper, we propose a new framework to track on-road pedestrians across multiple driving recorders. The framework is built upon the results of tracking under a single driving recorder. More specifically, we treat the problem as a multi-label classification task and determine whether a specific pedestrian belongs to one or several cameras' field of views by considering association likelihood of the tracked pedestrians . The likelihood is calculated based on the pedestrians' motion cues and appearance features, which are necessarily transformed via brightness transfer functions obtained by some available spatially overlapping views for compensating diversity of the cameras. When a pedestrian is leaving a camera's field of view, the proposed framework predicts and interpolates its possible moving trajectories, facilitated by open map service which can provide routing information. Experimental results show the robustness and effectiveness of the proposed framework in tracking pedestrians across several recorded driving videos. Moreover, based on the GPS locations, we can also reconstruct a 3-D visualization on a 3-D virtual real-world environment, so as to show the dynamic scenes of the recorded videos.

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Cited By

Ning TZhong BLiang QTang ZLi X(2024)Robust Tracking via Bidirectional Transduction With Mask InformationIEEE Transactions on Multimedia10.1109/TMM.2023.332149726(4308-4319)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TMM.2023.3321497
Wang GLi LZhu MZhao RZhang X(2024)Kernel based local matching network for video object segmentationMachine Vision and Applications10.1007/s00138-024-01524-435:3Online publication date: 25-Mar-2024
https://dl.acm.org/doi/10.1007/s00138-024-01524-4
Zhang HWang PChen ZZhang JLi L(2024)Target–distractor memory joint tracking algorithm via Credit Allocation NetworkMachine Vision and Applications10.1007/s00138-024-01508-435:2Online publication date: 9-Feb-2024
https://dl.acm.org/doi/10.1007/s00138-024-01508-4
Show More Cited By

Index Terms

On-Road Pedestrian Tracking Across Multiple Driving Recorders
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Tracking
      2. Computer vision tasks
        Scene understanding
2. Information systems
  1. Information systems applications

Index terms have been assigned to the content through auto-classification.

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cover image IEEE Transactions on Multimedia

IEEE Transactions on Multimedia Volume 17, Issue 9

Sept. 2015

270 pages

ISSN:1520-9210

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Copyright © 2015.

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IEEE Press

Publication History

Published: 01 September 2015

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Cited By

Ning TZhong BLiang QTang ZLi X(2024)Robust Tracking via Bidirectional Transduction With Mask InformationIEEE Transactions on Multimedia10.1109/TMM.2023.332149726(4308-4319)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TMM.2023.3321497
Wang GLi LZhu MZhao RZhang X(2024)Kernel based local matching network for video object segmentationMachine Vision and Applications10.1007/s00138-024-01524-435:3Online publication date: 25-Mar-2024
https://dl.acm.org/doi/10.1007/s00138-024-01524-4
Zhang HWang PChen ZZhang JLi L(2024)Target–distractor memory joint tracking algorithm via Credit Allocation NetworkMachine Vision and Applications10.1007/s00138-024-01508-435:2Online publication date: 9-Feb-2024
https://dl.acm.org/doi/10.1007/s00138-024-01508-4
Zhou XGuo PHong LLi JZhang WGe WZhang WOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)Reading relevant feature from global representation memory for visual object trackingProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3666598(10814-10827)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3666598
Zhou ZZhou XChen ZGuo PLiu QZhang W(2023)Memory Network With Pixel-Level Spatio-Temporal Learning for Visual Object TrackingIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2023.327231933:11(6897-6911)Online publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1109/TCSVT.2023.3272319
Wang FCao PWang XHe BSun F(2023)SiamADT: Siamese Attention and Deformable Features Fusion Network for Visual Object TrackingNeural Processing Letters10.1007/s11063-023-11290-555:6(7933-7950)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1007/s11063-023-11290-5
Li ZLiu SCheng ZYu ZFan H(2023)Object Tracking Algorithm Based on Dual Layer AttentionIntelligent Robotics and Applications10.1007/978-981-99-6486-4_1(3-14)Online publication date: 5-Jul-2023
https://dl.acm.org/doi/10.1007/978-981-99-6486-4_1
Wei YXiao HFeng DMu JYang J(2022)Aggregating Correlation Filter with Multiple Kernels Learning for Robust Visual Object TrackingProceedings of the 8th International Conference on Computing and Artificial Intelligence10.1145/3532213.3532305(603-609)Online publication date: 18-Mar-2022
https://dl.acm.org/doi/10.1145/3532213.3532305
Nakka KSalzmann M(2022)Universal, Transferable Adversarial Perturbations for Visual Object TrackersComputer Vision – ECCV 2022 Workshops10.1007/978-3-031-25056-9_27(413-429)Online publication date: 23-Oct-2022
https://dl.acm.org/doi/10.1007/978-3-031-25056-9_27
Hui LWang LTang LLan KXie JYang J(2022)3D Siamese Transformer Network for Single Object Tracking on Point CloudsComputer Vision – ECCV 202210.1007/978-3-031-20086-1_17(293-310)Online publication date: 23-Oct-2022
https://dl.acm.org/doi/10.1007/978-3-031-20086-1_17
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