Abstract
Smart city applications have increasingly relied on the internet of things (IoT) and edge computing to enable data collection, processing, and communication across distributed networks. Owing to the widespread adoption of IoT and visual object tracking technologies in various domains, traditional visual object tracking techniques are no longer suitable for IoT applications. This paper discusses how image recognition and visual target tracking can be used to enhance track and field training based on IoT technology and reduce coach workload. The purpose of this project aimed to analyze athlete movements, develop customized training plans for track and field athletes, and improve the quality and efficiency of training for track and on-field athletes. This is accomplished by utilizing the edge computing to track moving targets for tracking and field training images. Matching points are extracted from the track and field training images and incorporated into the field-of-view border. Using an invariant projection method based on color information, it is possible to accurately track the movement of the target by analyzing its center position and the distance between field-of-view boundaries. Moreover, a moving target recognition technique based on neural networks is employed for image recognition, utilizing an error back-propagation algorithm to facilitate target recognition in track and field training images. The proposed method is demonstrated to be highly accurate and efficient, thereby reducing the workload of coaches, enabling detailed movement analysis of athletes, and increasing the effectiveness of track and field training. This algorithm has been demonstrated to be more effective than other methods in terms of tracking-effect diagrams and evaluation criteria based on experimental results. Using the proposed algorithm, the occlusion problem can be effectively solved while ensuring real-time tracking performance.
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Li, J., Tian, S. & Charoenwattana, S. Smart IoT-based visual target enabled track and field training using image recognition. Soft Comput 27, 12571–12585 (2023). https://doi.org/10.1007/s00500-023-08820-7
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DOI: https://doi.org/10.1007/s00500-023-08820-7