Abnormal behavior recognition of elevator passengers based on improved SlowFast networks
Aims:This paper aims to solve the problem of the ineffective utilization of temporal features in video data of the algorithm for identifying passengers'abnormal behavior in elevator cabins.Methods:A SlowFast network algorithm based on residual branches and BiFormer improvement was proposed.This network structure took RGB video frames and residual frames as inputs,extracted feature information from multiple branches,integrated the spatiotemporal features of slow branches,fast branches,and residual branches to enhance the sensitivity to passenger abnormal behavior,and reduced the impact of background changes.Results:To verify the effectiveness of the network algorithm,a dataset of abnormal behavior of elevator passengers was used to validate the proposed network structure.Compared with the original SlowFast network,the improved network increased the recognition accuracy by 8.46％.Conclusions:The results showed that the proposed network algorithm could fully utilize the temporal dimension information in video frames and effectively improve the accuracy of identifying the abnormal behavior of elevator passengers.It has good recognition capability even inside elevator cabins with different background and illumination.
万方数据
维普
