联合语义提示和记忆增强的弱监督跳绳视频异常检测方法OA北大核心CSTPCD

The student evaluation enhancement platform collects millions of jump rope movement data points from primary and secondary school students for physical fitness assessment.However,in the collected jump rope videos,there are various abnormal videos that do not meet the shooting requirements,such as non-jump rope videos,and characters that do not appear in full body.This seriously affects the accuracy and robustness of the follow-up physical fitness assessment model of students.To solve these problems,this study proposes a weakly supervised video anomaly detection method that combines semantic cues and memory enhancement.First,the visual features of normal and abnormal skipping rope videos are extracted,and the normal and abnormal features are trained in pairs to enhance the model's perception of abnormal video features.Second,two self-supervised memory networks are designed to store and separate the features of normal and abnormal videos to further enhance the feature representation ability of the model.Finally,a prompt learning method is introduced to transfer the semantic prior knowledge of various skip rope exception types in a large-scale pre-training model to enhance the model's understanding of the semantic information of various exception types in the case of insufficient samples.The experimental results show that the AUC of the proposed method on the self-built Skip Rope Anomaly Detection(SRAD)dataset is 94.14%,which is 2.71 percentage points higher than that of the benchmark method,thereby exhibiting high accuracy.The proposed method is of great significance for realizing intelligent physical fitness evaluation and promoting educational assessment reform.