The traditional method of identifying abnormal behavior in athletes during long‐distance track and field events, particularly through video surveillance, has limitations such as misjudgments and oversight. To address these challenges, this study explores the use of artificial intelligence (AI) and intelligent surveillance technology for event safety management. However, existing video‐based abnormal behavior recognition methods face significant difficulties in adapting to marathon scenarios due to the complexities of outdoor surveillance footage. In response, this paper proposes innovative methods for recognizing abnormal behavior and identifying athletes using bib numbers, thereby enhancing the accuracy of behavior detection in marathon environments. Additionally, an AI‐powered system for athlete identification and abnormal behavior detection is introduced to improve event monitoring and safety. The contributions of this study include the development of an attention‐residual‐based abnormal behavior recognition algorithm, a rotation‐based object detection model for accurate athlete identification, and a deep learning–based intelligent recognition system for effective event management.
Junwei Zhu (Thu,) studied this question.