research-article

Automatic Basketball Detection in Sport Video Based on R-FCN and Soft-NMS

Authors:

Qiaokang Liang,

Yaonan Wang, and

Dan ZhangAuthors Info & Claims

CACRE2019: Proceedings of the 2019 4th International Conference on Automation, Control and Robotics Engineering

July 2019

Article No.: 9, Pages 1 - 6

https://doi.org/10.1145/3351917.3351970

Published: 19 July 2019 Publication History

Abstract

In basketball videos, the ball is always so small in the camera that its appearance feature is hard to be extracted. In this paper, we introduce a deep-learning technology to detect the basketball. Specifically, we train our basketball detection model based on the Region-based Fully Convolutional Networks (R-FCN) which uses the fully convolutional Residual Network (ResNet) as the backbone network. What's more, we apply some new techniques including Online Hard Example Mining (OHEM), Soft-NMS and multi-scale training strategy to achieve higher detection accuracy. In detail, the OHEM method can reduce the cost of fine-tuning during training by calculating the loss of the RoIs. Soft-NMS can reduce the false positive rate by decreasing the object detection score between the overlap object. And the multi-scale training can improve the detection performance by receiving the good feature from the object with different scale. Finally, we achieve a mean average precision (mAP) value of 89.7% on a public basketball dataset. It proves that applying the deep-learning approach to basketball detection is effective.

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https://doi.org/10.1109/TPAMI.2023.3273210
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CACRE2019: Proceedings of the 2019 4th International Conference on Automation, Control and Robotics Engineering

July 2019

478 pages

ISBN:9781450371865

DOI:10.1145/3351917

Editor:
Fumin Zhang

Copyright © 2019 ACM.

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 the author(s) 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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Sichuan University

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Association for Computing Machinery

New York, NY, United States

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Published: 19 July 2019

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CACRE2019: 2019 4th International Conference on Automation, Control and Robotics Engineering

July 19 - 21, 2019

Shenzhen, China

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Cited By

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https://doi.org/10.1109/TPAMI.2023.3273210
Sakabe HNakada Y(2023)Enhanced Method for Computing Optimal Dribbling Routes Using Tracking Data in Basketball2023 IEEE Ninth Multimedia Big Data (BigMM)10.1109/BigMM59094.2023.00009(11-18)Online publication date: 11-Dec-2023
https://doi.org/10.1109/BigMM59094.2023.00009
Yuan SZuo ZSun BLiu Y(2023)Research on Grasping Detection Method of Manipulator Based on SOLOV2Proceedings of 2022 International Conference on Autonomous Unmanned Systems (ICAUS 2022)10.1007/978-981-99-0479-2_51(550-561)Online publication date: 10-Mar-2023
https://doi.org/10.1007/978-981-99-0479-2_51
Russomanno TBlauberger PKolbinger OLam HSchmid MLames M(2022)Drone-Based Position Detection in Sports—Validation and ApplicationsFrontiers in Physiology10.3389/fphys.2022.85051213Online publication date: 17-Mar-2022
https://doi.org/10.3389/fphys.2022.850512
Zang YZhu H(2022)Multi-Hypothesis Joint Detection and Estimation with Worst-Case Mean Square Error2022 7th International Conference on Automation, Control and Robotics Engineering (CACRE)10.1109/CACRE54574.2022.9834114(288-295)Online publication date: Jul-2022
https://doi.org/10.1109/CACRE54574.2022.9834114
Moodley Tvan der Haar D(2022)Cricket Scene Analysis Using the RetinaNet ArchitectureProgress in Pattern Recognition, Image Analysis, Computer Vision, and Applications10.1007/978-3-030-93420-0_19(197-206)Online publication date: 1-Jan-2022
https://doi.org/10.1007/978-3-030-93420-0_19
Sano YNakada Y(2021)Visualization for Potential Pass Courses and Quantification for Offensive and Defensive Players in Basketball2021 International Conference on Engineering and Emerging Technologies (ICEET)10.1109/ICEET53442.2021.9659701(1-6)Online publication date: 27-Oct-2021
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Nelikanti AVenkata Rami Reddy GKaruna G(2021)An Optimization Based deep LSTM Predictive Analysis for Decision Making in CricketInnovative Data Communication Technologies and Application10.1007/978-981-15-9651-3_59(721-737)Online publication date: 3-Feb-2021
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