Abstract
In the basketball game, the accuracy and standardization of pitching are directly related to the score. So it is very important to analyze the pitching figure movement to have a better positioning of the fingers. There are limited techniques to recognize the movement. The human motion recognition method is one of them. It utilizes the spatiotemporal image segmentation and interactive region detection to recognize images of pitching finger movement of basketball players. This method has a limitation that the symmetrical information of the human body and sphere cannot be excavated, which leads to certain errors in recognition effect. This paper presents a method of recognizing pitching finger movement of basketball players based on symmetry algorithm, constructs an acquisition model, carries out edge contour detection and adaptive feature segmentation of images of pitching finger movement of basketball players, and uses a fixed threshold to segment finger image to extract players’ hand contour and locate the middle axis of the finger. On this basis, the symmetry recognition method based on nematode recognition algorithm is used to recognize the symmetry of basketball pitching finger movement image and complete the accurate recognition of basketball pitching finger movement image. The experimental results show that the proposed method can effectively recognize the basketball player’s finger movement image. The average recognition accuracy is 98%, the growth rate of recognition speed is 98%, and the maximum recognition time is 12 s. The robustness of the proposed method is 0.45.
Highlights
In the basketball game, the accuracy and standardization of pitching are directly related to the score, which is of great significance to the accurate recognition and judgment of the finger-stroke motion image of basketball pitching
A human motion recognition method based on spatiotemporal image segmentation and interactive region detection is proposed [4, 5], which detects human contours in the video stream and segments them into key regions, expands the segmentation to include nonhuman objects interacting with the human body
The spatiotemporal histogram of gradient direction (HOG) and histogram of optical flow field (HOF) descriptor were used to represent the static and dynamic characteristics of key segments, and codebook is constructed by K-means algorithm
Summary
The accuracy and standardization of pitching are directly related to the score, which is of great significance to the accurate recognition and judgment of the finger-stroke motion image of basketball pitching. A human motion recognition method based on spatiotemporal image segmentation and interactive region detection is proposed [4, 5], which detects human contours in the video stream and segments them into key regions, expands the segmentation to include nonhuman objects interacting with the human body. The symmetrical information of body and sphere leads to some errors in recognition effect Another motion recognition method combines multipose estimation features [6] and uses the obtained multimotion models to estimate the posture of any image to obtain multigroup posture feature information of the image; each group of feature information includes key point information and posture scoring, because the method is based on multimotion models to recognize images, the recognition speed is slow, and the efficiency is low. The depth image is projected in three projection planes; the Gabor features are extracted from three projection maps, and these features are utilized to train extreme learning machine (ELM) classifier and complete motion classification, but the Wireless Communications and Mobile Computing
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