An Unsupervised Person Search Method for Video Surveillance
Deying Feng, Jie Yang, Yanxia Wei, Hairong Xiao, Laigang Zhang · 2022
We propose an unsupervised person search method for video surveillance. This method considers both the spatial features of persons within each frame and the temporal relationship of the same person among different frames. Thus, the spatial features are extracted by region convolutional neural network, and the temporal relationship is organized by gate recurrent unit. The spatio-temporal features are generated by the following average pooling layer and indexed by locality sensitive hashing. A surveillance video database is constructed to evaluate the proposed method, and the experimental results demonstrate that our method improves the search accuracy by utilizing the spatio-temporal features.