Tracking Method for Alpine Skiing Based on Hybrid Deep Learning and Evolutionary Chimp Optimization Algorithm

Abstract

Tracking athletes in high-speed outdoor sports like alpine skiing causes substantial difficulties because of ever-changing movements, environmental variability, and the limitations of traditional tracking technologies, such as intrusive sensors and single-view camera setups. This study proposes a hybrid approach for tracking alpine skiing activities by combining YOLO-v8 with an evolutionary version of the chimp optimization algorithm (CHOA-EVOL) for optimizing hyperparameters. The primary goal of this research is to enhance the CHOA to optimally adjust the hyperparameters of YOLO-v8, consequently addressing the drawbacks of outdoor sports tracking technology. This hybrid model integrates data from unmanned aerial vehicles (UAVs) and terrestrial cameras to better understand athletes’ rapid rotating motion. The suggested approach is extensively tested and validated using advanced algorithms with the UAV123 dataset and a recently developed alpine skiing dataset (ASD). The results have shown that our proposed approach can achieve high precision and robustness.