Multi-Query Person Search with Transformers

We propose a transformer-based multi-query person search (MQPS) method that jointly performs person detection and person re-identification (re-id) in an end-to-end framework. Most existing person search methods employ hand-crafted components and involve multiple steps and stages to detect and identify the target person, which are computationally inefficient and brutal to generalise to different datasets. The recent advance in end-to-end object detection with transformers, mainly the DETR family, employ object queries to learn objects and directly predict a set of bounding boxes and object classes. However, this approach uses one object query per object so that the detected object is centred around the object spatial location, which is not ideal for small and occluded objects during feature representation learning. Therefore, we propose a multi-query method for person detection and person feature representation learning. Specifically, MQPS utilises multiple adjacent object queries to learn a target person object with multi-scale features. Moreover, to improve the feature representation learning of intra-identity objects, we employ a margin ranking loss to bring closer the intra-identity person instances in the feature space. Experiments on CUHK-SYSU and PRW datasets demonstrate the effectiveness of the proposed method.