计算机工程2025，Vol.51Issue(11)：112-122,11.DOI:10.19678/j.issn.1000-3428.0069713
基于改进TimeSformer算法的人体异常行为识别研究
Research on Abnormal Human Behavior Recognition Based on Improved TimeSformer Algorithm
摘要
Abstract
Research on abnormal human behavior is an important safeguarding task to deal with potential dangers and emergencies.In view of the fuzzy definition of abnormal human behavior and the lack of standard datasets,this study defines six high-frequency abnormal human behaviors based on life scenarios-namely Headache,Fall,Twitch,Lumbago,Punch,and Kick-and independently constructs a dataset known as HABDataset-6.The attention mechanism in TimeSformer can be used to process this self-built dataset;however,it suffers from high loss and incomplete time series modeling,making it difficult to extract features from complex samples.Therefore,this study uses the Accelerating Stochastic Gradient Descent(ASGD)optimization algorithm to improve the cross-entropy loss,that is,a CAS module is proposed that reduces the loss value of the original algorithm.Second,a Temporal Shift Module(TSM)is embedded in the backbone network of the original algorithm to improve the perception ability of the time series to extract better features for model training.Then,the study integrates CAS and TSM and proposes the TS-AT algorithm,achieving good results on the self-built dataset with a reasoning accuracy of more than 80％for each behavior category.The usability of the TS-AT algorithm is tested on the public dataset,UCF-10,and on the elderly abnormal behavior data,and it achieves average test accuracies of 99％and 84％,respectively,exceeding those of advanced algorithms.These results show that the TS-AT algorithm has higher accuracy and good robustness in identifying abnormal human behavior and is expected to improve the ability to respond to potential dangers and emergencies and further ensure people's safety and health.关键词
人体异常行为/TimeSformer算法/时间序列/优化算法/时间偏移模块Key words
abnormal human behavior/TimeSformer algorithm/time series/optimization algorithm/Temporal Shift Module(TSM)分类
计算机与自动化引用本文复制引用
廖晓群,徐清钏,杨浩东,李丹,薛亚楠..基于改进TimeSformer算法的人体异常行为识别研究[J].计算机工程,2025,51(11):112-122,11.基金项目
中国高校产学研创新基金(2021KSA05005). （2021KSA05005）
