Abstract
For long-distance running tactics, a computer vision-based motion correction solution is presented. The depth information is merged into the KCF algorithm to enhance it, which overcomes the classic KCF method’s incapacity to tackle the tracking drift issue caused by occlusion and extracts the technical features of long-distance running motions. Based on computer vision, the posture area of long-distance runners is detected, and the technical movements of long-distance runners are recognized. Calculate the centroid coordinates of the wrong technical movement correction area in long-distance running, and generate a tracking image of the wrong technical movement in long-distance running. The foreground and background information of the image is separated by the optical flow feature of machine vision, and the motion trajectory of the long-distance running error technique is extracted, and the motion correction of the long-distance running technique based on computer vision is realized. The experimental results show that the method in this paper has better accuracy in extracting long-distance running motion features and can accurately identify and correct the technical movements of the long-distance running, the technical movements of the head, and the technical movements of the body balance, and the correction efficiency is high.
Highlights
Observing the runners’ running stance, it is concluded that the wrong technical movements of long-distance running mainly include the soles of the feet on the ground, the toes on the ground, the stride is too large, the inside and outside “sigma feet,” and bowing or raising the head. ese wrong longdistance running postures will affect the coordination of the human body and increase the consumption of physical strength
In order to allow more fitness athletes to run with the correct posture, this paper proposes a computer visionbased action correction method for long-distance running techniques
Long-distance runners’ motion characteristics are extracted, the rationality judgement threshold of posture characteristics is determined, and it is applied to the recognition of longdistance running technical movements, and optical flow characteristics are introduced into machine vision technology to determine long-distance runners’ errors. e technical action range and azimuth angle are extracted, and the characteristics of the technical actions of long-distance running mistakes are extracted, followed, and corrected to achieve the purpose of correcting the technical actions of long-distance running mistakes
Summary
Among aerobic exercises, running is a popular physical activity. Running has the ability to improve not just cardiopulmonary function and muscular strength, resulting in a body-strengthening impact. In order to allow more fitness athletes to run with the correct posture, this paper proposes a computer visionbased action correction method for long-distance running techniques. It can detect target occlusion, minimise negative sample noise, and increase the tracking accuracy of longdistance running technology thanks to an upgraded KCF algorithm that incorporates depth information. Long-distance runners’ motion characteristics are extracted, the rationality judgement threshold of posture characteristics is determined, and it is applied to the recognition of longdistance running technical movements, and optical flow characteristics are introduced into machine vision technology to determine long-distance runners’ errors. Long-distance runners’ motion characteristics are extracted, the rationality judgement threshold of posture characteristics is determined, and it is applied to the recognition of longdistance running technical movements, and optical flow characteristics are introduced into machine vision technology to determine long-distance runners’ errors. e technical action range and azimuth angle are extracted, and the characteristics of the technical actions of long-distance running mistakes are extracted, followed, and corrected to achieve the purpose of correcting the technical actions of long-distance running mistakes
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