In modern fields such as home,commerce,and industry,cross-border behavior has become a common security risk.To address the issues of time-consuming performance and difficulty in adapting to complex scenes in the current algorithm for identifying pedestrian abnormal crossing behavior,an embedded system for identifying pedestrian abnormal crossing behavior is proposed.The YOLO algorithm is used for object detection,and the ByteTrack multi-objective tracking algorithm is used to track and associate the detected targets.The area monitoring algorithm is combined with the tracking trajectory of the multi-objective tracking algorithm to achieve accurate identification of crossing behavior.The experiment results have shown that the proposed system can be deployed on smartphones equipped with the Aidlux system,achieving accurate recognition of human behavior beyond boundaries in different scenarios and issuing alarm signals,meeting the application requirements of most scenarios.