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Posture Recognition and Behavior Tracking in Swimming Motion Images under Computer Machine Vision

机译：计算机机视觉下游泳运动图像中的姿态识别与行为

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摘要

This study is to explore the gesture recognition and behavior tracking in swimming motion images under computer machine vision and to expand the application of moving target detection and tracking algorithms based on computer machine vision in this field. The objectives are realized by moving target detection and tracking, Gaussian mixture model, optimized correlation filtering algorithm, and Camshift tracking algorithm. Firstly, the Gaussian algorithm is introduced into target tracking and detection to reduce the filtering loss and make the acquired motion posture more accurate. Secondly, an improved kernel-related filter tracking algorithm is proposed by training multiple filters, which can clearly and accurately obtain the motion trajectory of the monitored target object. Finally, it is proposed to combine the Kalman algorithm with the Camshift algorithm for optimization, which can complete the tracking and recognition of moving targets. The experimental results show that the target tracking and detection method can obtain the movement form of the template object relatively completely, and the kernel-related filter tracking algorithm can also obtain the movement speed of the target object finely. In addition, the accuracy of Camshift tracking algorithm can reach 86.02%. Results of this study can provide reliable data support and reference for expanding the application of moving target detection and tracking methods.
机译：本研究是在计算机机视觉下探讨游泳运动图像中的手势识别和行为跟踪，并扩大基于计算机机视觉的移动目标检测和跟踪算法的应用。通过移动目标检测和跟踪，高斯混合模型，优化的相关滤波算法和CAPShift跟踪算法来实现目标。首先，将高斯算法引入目标跟踪和检测以降低滤波损耗，并使所获取的运动姿势更准确。其次，通过训练多个滤波器提出了一种改进的内核相关滤波器跟踪算法，其可以清楚准确地获得受监视的目标对象的运动轨迹。最后，建议将卡尔曼算法与CAPShift算法结合以进行优化，可以完成移动目标的跟踪和识别。实验结果表明，目标跟踪和检测方法可以相对完全获得模板对象的运动形式，并且内核相关的滤波器跟踪算法还可以精细地获得目标物体的移动速度。此外，CACSHIFT跟踪算法的准确性达到86.02％。该研究的结果可以为扩大移动目标检测和跟踪方法的应用提供可靠的数据支持和参考。

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