Abstract
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.
Highlights
Computer vision technology has developed rapidly in recent years. e human visual system can be simulated by using the computer vison technology, which can realize the processing of relevant information from the outside world, of which moving target tracking and object recognition are popular research directions in the field of computer vision [1, 2]
E figure indicates the tracking accuracy of the optimized Camshift algorithm before and after occultation is significantly higher than that of the conventional Camshift algorithm, and the tracking accuracy of the conventional Camshift algorithm drops from 68.31% to 54.85% under a severe occultation. e tracking accuracy of the conventional Camshift algorithm decreases from 64.86% to 53.79%
Complexity shows the effectiveness of the moving target detection method in target information extraction. e edge information of the swimming motion image of medaka is missing, which is analyzed through the obtained binary image. en, the outline circumscribed rectangle is obtained, and it is possible to achieve the extraction of complete target feature information
Summary
Computer vision technology has developed rapidly in recent years. e human visual system can be simulated by using the computer vison technology, which can realize the processing of relevant information from the outside world, of which moving target tracking and object recognition are popular research directions in the field of computer vision [1, 2]. E human visual system can be simulated by using the computer vison technology, which can realize the processing of relevant information from the outside world, of which moving target tracking and object recognition are popular research directions in the field of computer vision [1, 2]. Based on the study of fish behaviors, it is possible to explore the laws of their activities [3, 4]. At present, related scholars have conducted many research studies on the tracking of moving targets in computer vision. Huang et al discussed the performance of the Kalman filter in target tracking based on computer vision and found that this method has high efficiency, and the adaptive particle filter has high robustness and high precision, which provide references for fruit recognition and Complexity tracking as well as robot navigation and control [6]. Desai and Lee introduced computer vision to explore the target tracking of drones [8]
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