# An Efficient Person Search Method Using Spatio-Temporal Features for Surveillance Videos +
## 2 Related Work
### Person Search Method  
Person search is developed based on person re-identification and fills the gap between person re-identification and practical applications. According to different person detection methods used in the search process, the existing person search methods can be divided into two-stage search methods [12; 22; 23; 24] and end-to-end search methods [8; 9; 10; 11; 25; 26; 27; 28; 29; 30; 31].  
Two-stage person search methods divide person detection and person re-identification into two separate stages. [PERSON] [12] explores region convolutional neural network as person detector and introduces the confidence weighted similarity metric into detection scores. [PERSON] proposes a mask-guided two-stream CNN model [22] to obtain enriched representations. Similarly, [PERSON] proposes a cross-level semantic alignment deep learning method [23] to learn more discriminative identity feature representation in the detection stage. [PERSON] proposes a task-consistent two-stage framework [24], which consists of an identity-guided query detector and a detection results adapted re-ID model.  
End-to-end person search methods integrate person detection and re-identification in the same framework. He proposes a detection and re-identification integration net [25] and utilizes Siamese architecture in the re-identification stage. [PERSON] also uses a Siamese network with an additional instance-aware branch in the bi-directional interaction network [26]. [PERSON] proposes an align-and-park network [27] to enhance the robustness of person search. [PERSON] presents a feature-aligned person search framework [28], which is the first anchor-free network to tackle misalignment issues. [PERSON] proposes a decoupled and memory-reinforced network [29] to reconcile the conflicts of multiple objectives between detection and identification sub-tasks. [PERSON] proposes diverse knowledge distillation and spatial-invariant augmentation [30] to assist end-to-end framework. [PERSON] proposes a sequential end-to-end network [31] to extract superior features. [PERSON] introduces a cascade occluded attention transformer [32] into the end-to-end framework, which provides a discriminative coarse-to-fine representation for person detection and re-identification.  
In addition, some person search methods are proposed for video datasets. [PERSON] explores visual and temporal links of person identities by progressive propagation [33], but this method is only trained on a movie dataset. [PERSON] presents a dataset for audiovisual person search [34], but this dataset consists of face tracks and voice segments which are not related to surveillance videos. [PERSON] provides a P-DESTRE dataset [35] collected by aerial devices for person detection, tracking, and re-identification. [PERSON] employs frame differencing and MACH filters [36] to detect persons in secure areas, and [PERSON] presents a multi-behavioral social particle filter [37] to track moving persons from a moving robot. [PERSON] proposes an unsupervised video hashing method [38], but this method takes each video frame as a whole. Since this method does not detect persons in the videos, the spatial features of persons are affected by complex backgrounds.  
Person search methods achieve good performance to some extent, but these methods still have some limitations. First, due to public privacy, there are few surveillance videos publicly available for person search, which limits the development of person search methods. Second, to the best of our knowledge, most of the person search methods are trained on CHUK-SYSU and PRW datasets. Because these two datasets do not possess the temporal property of videos, the person search methods trained on these datasets cannot be directly utilized in real-world surveillance. Third, compared with image-based person search methods, there are relatively few video-based person search methods. For surveillance videos, the performance of person search methods needs to be improved by incorporating the temporal relationship of persons between adjacent frames.