{"title":"基于深度学习和进化黑猩猩优化算法的高山滑雪跟踪方法","authors":"Xiaohua Wu, Yongtao Shi, Mohammad Khishe","doi":"10.1155/cplx/6829161","DOIUrl":null,"url":null,"abstract":"<div>\n <p>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.</p>\n </div>","PeriodicalId":50653,"journal":{"name":"Complexity","volume":"2025 1","pages":""},"PeriodicalIF":1.7000,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/cplx/6829161","citationCount":"0","resultStr":"{\"title\":\"Tracking Method for Alpine Skiing Based on Hybrid Deep Learning and Evolutionary Chimp Optimization Algorithm\",\"authors\":\"Xiaohua Wu, Yongtao Shi, Mohammad Khishe\",\"doi\":\"10.1155/cplx/6829161\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n <p>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.</p>\\n </div>\",\"PeriodicalId\":50653,\"journal\":{\"name\":\"Complexity\",\"volume\":\"2025 1\",\"pages\":\"\"},\"PeriodicalIF\":1.7000,\"publicationDate\":\"2025-04-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1155/cplx/6829161\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Complexity\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1155/cplx/6829161\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Complexity","FirstCategoryId":"5","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1155/cplx/6829161","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
Tracking Method for Alpine Skiing Based on Hybrid Deep Learning and Evolutionary Chimp Optimization Algorithm
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.
期刊介绍:
Complexity is a cross-disciplinary journal focusing on the rapidly expanding science of complex adaptive systems. The purpose of the journal is to advance the science of complexity. Articles may deal with such methodological themes as chaos, genetic algorithms, cellular automata, neural networks, and evolutionary game theory. Papers treating applications in any area of natural science or human endeavor are welcome, and especially encouraged are papers integrating conceptual themes and applications that cross traditional disciplinary boundaries. Complexity is not meant to serve as a forum for speculation and vague analogies between words like “chaos,” “self-organization,” and “emergence” that are often used in completely different ways in science and in daily life.