计算机工程2024，Vol.50Issue(7)：87-95,9.DOI:10.19678/j.issn.1000-3428.0069415
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
Weakly Supervised Video Anomaly Detection Method for Rope Skipping Combined with Semantic Prompts and Memory Enhancement
摘要
Abstract
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.关键词
视频异常检测/提示学习/记忆网络/弱监督学习/身体素质测评Key words
video anomaly detection/prompt learning/memory network/weakly supervised learning/physical fitness assessment分类
信息技术与安全科学引用本文复制引用
周炫余,吴莲华,郑勤华,肖天星,王紫璇,张思敏..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[J].计算机工程,2024,50(7):87-95,9.基金项目
国家重点研发计划(2021YFC3340800) （2021YFC3340800）
国家自然科学基金(62007007,61703155) （62007007,61703155）
湖南省自然科学基金(2023JJ30415,2022JJ30395). （2023JJ30415,2022JJ30395）
