research-article

Using FTOC to track shuttlecock for the badminton robot

Authors:

Zhiguang CaoAuthors Info & Claims

Volume 334, Issue C

Pages 182 - 196

https://doi.org/10.1016/j.neucom.2019.01.023

Published: 21 March 2019 Publication History

Abstract

Sport video analysis is gaining popularity recently owing to its importance in understanding sports and improving the performance of athletes. In this paper we focus on shuttlecock tracking algorithm. Particularly, a novel fast tracking based on object center (i.e., FTOC) method by fusing heterogeneous cues and AdaBoost algorithm are proposed to improve the tracking performance for a robot. Experimental results show that the proposed FTOC tracking method performs favorably against many other popular tracking approaches, such as TLD, MIL, KCF, DCF_CA, SMAF_CA, KCC, DSN, COKCF, etc., in term of speed, accuracy, and robustness, especially in challenging scenarios such as scale variations and background clutter. We further demonstrate the feasibility of the FTOC algorithm in a real-time ZED binocular camera based 3D shuttlecock tracking system for a robot.

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Cited By

Zeng W(2022)Changes in Physical Function of Shuttlecock Players after Short-Term Intensive Training based on Data MiningComputational Intelligence and Neuroscience10.1155/2022/81535212022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/8153521
Xipeng ZPeng ZYecheng C(2022)Research on Badminton Teaching Technology Based on Human Pose Estimation AlgorithmScientific Programming10.1155/2022/46643882022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/4664388
Gao S(2022)The Application of Wireless Network-Based Artificial Intelligence Robots in Badminton Teaching and TrainingComputational Intelligence and Neuroscience10.1155/2022/39103072022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/3910307
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Index Terms

Using FTOC to track shuttlecock for the badminton robot
1. Computer systems organization
  1. Embedded and cyber-physical systems
    1. Robotics
2. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Tracking
      2. Computer vision tasks
    2. Planning and scheduling
      1. Robotic planning

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

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Published In

cover image Neurocomputing

Neurocomputing Volume 334, Issue C

Mar 2019

276 pages

ISSN:0925-2312

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Elsevier B.V.

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 21 March 2019

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Cited By

Zeng W(2022)Changes in Physical Function of Shuttlecock Players after Short-Term Intensive Training based on Data MiningComputational Intelligence and Neuroscience10.1155/2022/81535212022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/8153521
Xipeng ZPeng ZYecheng C(2022)Research on Badminton Teaching Technology Based on Human Pose Estimation AlgorithmScientific Programming10.1155/2022/46643882022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/4664388
Gao S(2022)The Application of Wireless Network-Based Artificial Intelligence Robots in Badminton Teaching and TrainingComputational Intelligence and Neuroscience10.1155/2022/39103072022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/3910307
Hao LZhi JZhu WZhou L(2022)Research on Badminton Player’s Step Training Model Based on Big Data and IoT NetworksSecurity and Communication Networks10.1155/2022/19723892022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/1972389
Li QGarg SNie JLi XLiu RCao ZHossain M(2021)A Highly Efficient Vehicle Taillight Detection Approach Based on Deep LearningIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2020.302742122:7(4716-4726)Online publication date: 1-Jul-2021
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Guo JLiu YWang JZeng CHuang JLiu C(2020)The Design of Compact Robotic-Assisted Needle Position System with MPC-Based Remote ControlComplexity10.1155/2020/54060842020Online publication date: 1-Jan-2020
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Zhang XChen SCai SXiong XHu ZJu Z(2019)The Action Control Model for Robotic Fish Using Improved Extreme Learning MachineComplexity10.1155/2019/74560312019Online publication date: 25-Feb-2019
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