Research on Real-Time Detection of Sprint Error Based on Visual Features and Internet of Things

The current sprint error detection methods do not consider the analysis of the visual characteristics of sprint error, which leads to low detection accuracy, long detection time, and poor detection stability. To overcome this defect, inspired by Internet of Things technology, a real-time sprint error detection method based on visual characteristics is proposed. Based on the basic principle of RFID action perception, the original phase data is preprocessed, the channel parameters are selected, and the tag layout is optimized to form the action-oriented feature. Based on the three-dimensional visual features, the three-dimensional coordinate points of the sports field are determined, and the movement features of the sprint are extracted and formally described. Based on the analysis of the visual characteristics of sprint errors, the block pheromones of single frame sprint motion edge contour are obtained for clustering, and the sprint errors’ feature information is obtained and filtered. Sift technology is used to obtain the boundary contour line and implement the corner characteristics. The Hessian matrix of contour wave domain edge detection is used to calculate the contour wave domain matrix of the image and draw the contour curve of the image of sprint error action to realize the detection of sprint error action. The experimental results show that the proposed method has good stability and can effectively improve the detection accuracy and shorten the detection time.