Design of Query Based Gallery Selector and Mask-Aware Loss for Person Search

Person search is a challenging computer vision task that aims to simultaneously locate and identify a query person from panoramic images. To address the issue of scene similarity and its impact on search accuracy and efficiency, we propose a query based gallery selector module that employs cosine similarity to calculate the similarity between candidate images in the gallery and the query persons feature embedding, then selects and reorders images in the gallery based on their similarity to the query person, thus improving the accuracy and efficiency of searching. Furthermore, we introduce a mask-aware mechanism that improves the localization loss function for predicted bounding boxes. During training, the network is guided to increase its robustness in occluded scenarios. Experimental results on public person search datasets PRW and CUHK-SYSU demonstrate the effectiveness of our proposed method.