# An Efficient Person Search Method Using Spatio-Temporal Features for Surveillance Videos +
Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).  
Footnote †: This paper is an extended version of the paper “An Unsupervised Person Search Method for Video Surveillance Surveillance” published in 2022 8 th International Conference on Computing and Artificial Intelligence (ICCAI 2022).  
are not available, and the target person is searched for in unknown surveillance videos, which limits the practical application of person re-identification methods.  
To bridge the gap between person re-identification and practical application, some person search methods have been studied. Unlike person re-identification, person search methods need to detect persons in unknown images or videos and identify them in large-scale datasets. Recently, the end-to-end person search framework [8] has been widely used in person search methods. In this framework, person detection and person re-identification are jointly handled in a single convolutional neural network. An Online Instance Matching (OIM) loss function is also introduced to ensure training efficiency. After that, OIM is improved by some person search methods [9; 10; 11] to increase the similarity of the same person. Although these methods have achieved good performance to some extent, they are not suitable for surveillance videos. This is because these methods are trained on CHUK-SYSU [8] and PRW datasets [12], and only explore single images in the two datasets. They do not consider the temporal relationship of adjacent frames in the video, which reduces the search accuracy of surveillance videos.  
In this work, we propose an efficient person search method that employs spatio-temporal features. Since surveillance videos contain a series of frames that are temporally correlated, our method not only employs the spatial features of persons in each frame, but also utilizes the temporal relationship of the same person between adjacent frames. For this purpose, the bounding boxes of persons are detected by Yolo network, and then the corresponding spatial features are obtained using ResNet-50 model. After that, Gated Recurrent Unit (GRU) is used to calculate the temporal correlation of spatial features between adjacent frames, and an average pooling layer is added to generate a spatio-temporal feature for each person. Finally, to ensure search efficiency, Locality Sensitive Hashing (LSH) is explored to organize a large number of spatio-temporal features. To verify our method, we also construct a real-world surveillance video database, which contains video clips of different backgrounds, viewpoints, and person identities.  
The rest of this paper is organized as follows. Section 2 reviews the related work in the area of person re-identification and person search. Section 3 details our proposed methods using spatio-temporal features. Section 4 presents the experimental results to evaluate our method. Section 5 summarizes this paper.