基于CNN和Yolov3的乒乓球视频多目标检测

W. Lia, Jie Cuib
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引用次数: 0

摘要

为了探索卷积神经网络(CNN)对乒乓球运动员和球的检测效果,解决乒乓球运动员训练数据准确率低、泛化能力弱的问题,本文对乒乓球运动员的视频进行了分析。为了检测视频中的多个目标,包括运动员和球,我们使用了Yolov3作为深度学习框架,在处理图像时使用CNN作为自动检测方法。我们对视频数据进行训练和测试,通过在Yolov3模型的基础上修改其模型,提高目标检测的稳定性和准确性。最后,我们稳定地检测了乒乓球视频中运动员和球的运动轨迹,准确率在0.8以上。
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Multi-Target Detection of Table Tennis Video Based on CNN and Yolov3
: In order to explore the effect of convolutional neural network (CNN) on the detection of athletes and balls in table tennis, and to solve the problems of low accuracy and weak generalization ability of table tennis athletes training data, this paper analyzed videos of table tennis athletes. For the detection of multiple targets in the videos, including athletes and balls, we used Yolov3 as a deep learning framework, and CNN as an automatic detection method when processing images. We trained and test the video data to improve the stability and accuracy of target detection, through modifying its model on the basis of the Yolov3 model. Finally, we detect the movement trajectories of athletes and balls in table tennis videos stably, and the accuracy is above 0.8.
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