Deep Learning Based Target Tracking Algorithm Model for Athlete Training Trajectory

Abstract

The main function of the athlete tracking system is to collect the real-time competition data of the athletes. Deep learning is a research hotspot in the field of image and video. With the rapid development of science and technology, it has not only made a breakthrough in theory, but also achieved excellent results in practical application. SiamRPN (Siamese Region Proposal Network) is a single target tracking network model based on deep learning, which has high accuracy and fast operation speed. However, in long-term tracking, if the target is completely obscured and out of the sight of SiamRPN, the tracking of the network will be invalid. Considering the difficulty of long-term tracking, the algorithm is improved and tested by introducing channel attention mechanism and local global search strategy into SiamRPN. Experimental results show that this algorithm has higher accuracy and prediction average overlap rate than the original SiamRPN algorithm when performing tracking tasks on long-term tracking sequences. At the same time, the improved algorithm can still achieve good results in the case of target disappearance and other challenging factors. This study provides an important reference for the coaches of deep learning to realize long-term tracking of athletes.

Disconnector Fault Diagnosis Based on Multi-Granularity Contrast Learning

“…Data augmentation attempts to improve the generalization ability of a trained model by reducing overfitting and expanding the decision boundary of the model [12]. The need for generalization is especially important for real-world data, which can help networks overcome small [13] or category-imbalanced datasets [14,15]. Most time series data augmentation techniques are based on random transformations of training data, such as adding random noise [16], slicing or cropping [17], scaling [18], random warping in the time dimension [13,15], frequency warping [19], etc.…”