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Distractor-Aware Tracker with a Domain-Special Optimized Benchmark for Soccer Player Tracking

Published: 01 September 2021 Publication History

Abstract

Player tracking in broadcast soccer videos has received widespread attention in the field of sports video analysis, however, we note that there is not a suitable tracking algorithm specifically for soccer video, and the existing benchmarks used for soccer player tracking cover few scenarios with low difficulties. From the observation of the soccer scene that interference and occlusion are knotty problems because the distractors are extremely similar to the targets, a distractor-aware player tracking algorithm and a high-quality benchmark for soccer play tracking (BSPT) have been presented. The distractor-aware player tracking algorithm is able to perceive semantic information about distracting players in the background by similarity judgment, the semantic distractor-aware information is encoded into a context vector and is constantly updated as the objects move through a video sequence. Distractor-aware information is then appended to the tracking result of the baseline tracker to improve the intra-class discriminative power. BSPT contains a total of 120 sequences with rich annotations. Each sequence covers 8 specialized frame-level attributes from soccer scenarios and the player occlusion situations are finely divided into 4 categories for a more comprehensive comparison. In the experimental section, the performance of our algorithm and the other 14 compared trackers are evaluated on BSPT with detailed analysis. Experimental results reveal the effectiveness of the proposed distractor-aware model especially under the attribute of occlusion. The BSPT benchmark and raw experimental results are available on the project page at http://media.hust.edu.cn/BSPT.htm.

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Cited By

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  • (2024)EIoU-distance loss: an automated team-wise player detection and tracking with jersey colour recognition in soccerConnection Science10.1080/09540091.2023.229199136:1Online publication date: 3-Feb-2024
  • (2024)A large-scale multivariate soccer athlete health, performance, and position monitoring datasetScientific Data10.1038/s41597-024-03386-x11:1Online publication date: 30-May-2024
  • (2024)Soccer match broadcast video analysis method based on detection and trackingComputer Animation and Virtual Worlds10.1002/cav.225935:3Online publication date: 29-May-2024
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  1. Distractor-Aware Tracker with a Domain-Special Optimized Benchmark for Soccer Player Tracking

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    cover image ACM Conferences
    ICMR '21: Proceedings of the 2021 International Conference on Multimedia Retrieval
    August 2021
    715 pages
    ISBN:9781450384636
    DOI:10.1145/3460426
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 01 September 2021

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    Author Tags

    1. benchmark
    2. computer vision
    3. dataset
    4. distractor aware
    5. soccer player tracking
    6. sports video analysis
    7. visual object tracking

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    • National Key Research and Development Program of China

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    Overall Acceptance Rate 254 of 830 submissions, 31%

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    Cited By

    View all
    • (2024)EIoU-distance loss: an automated team-wise player detection and tracking with jersey colour recognition in soccerConnection Science10.1080/09540091.2023.229199136:1Online publication date: 3-Feb-2024
    • (2024)A large-scale multivariate soccer athlete health, performance, and position monitoring datasetScientific Data10.1038/s41597-024-03386-x11:1Online publication date: 30-May-2024
    • (2024)Soccer match broadcast video analysis method based on detection and trackingComputer Animation and Virtual Worlds10.1002/cav.225935:3Online publication date: 29-May-2024
    • (2022)SoccerNet-Tracking: Multiple Object Tracking Dataset and Benchmark in Soccer Videos2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)10.1109/CVPRW56347.2022.00393(3490-3501)Online publication date: Jun-2022

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