Sports Video Motion Direction Detection and Target Tracking Algorithm Based on Convolutional Neural Network

Abstract

In order to effectively detect and monitor athletes and record various motion data of targets, the study suggests a study of target tracking algorithms to detect the direction of motion video sports movement based on the neural network. A class of feedforward neural networks with convolutional computation and deep structure is one of the representative algorithms of deep learning. Firstly, the athlete image is obtained from the video frame; combined with the nonathlete image to construct the training set, use the bootstrapping algorithm to train the convolutional neural network classifier. In the case of input picture frames, pyramids of different scales are then constructed by subsampling, and the location of many candidate athletes is detected by a neural network of disruption. Finally, these centers calculate the center of gravity of the athletes, find the athlete to represent the candidate, and determine the location of the final athlete through a local search process. The results of the experiment show that the proposed scheme of 6000 frames in the two game videos is compared with the AdaBoost scheme, and the detection rate of the proposed scheme is 75.41% to calculate the average detection accuracy and false alarm speed of all players. The detection rate is higher than the AdaBoost scheme. Therefore, this scheme has a high detection rate and low false positives.