DAAPS: A Deformable-Attention-Based Anchor-Free Person Search Model

Person Search is a task involving pedestrian detection and person re-identification, aiming to retrieve person images matching a given objective attribute from a large-scale image library. The Person Search models need to understand and capture the detailed features and context information of smaller objects in the image more accurately and comprehensively. The current popular Person Search models, whether end-to-end or two-step, are based on anchor boxes. However, due to the limitations of the anchor itself, the model inevitably has some disadvantages, such as unbalance of positive and negative samples and redundant calculation, which will affect the performance of models. To address the problem of fine-grained understanding of target pedestrians in complex scenes and small sizes, this paper proposes a Deformable-Attention-based Anchor-free Person Search model (DAAPS). Fully Convolutional One-Stage (FCOS), as a classic Anchor-free detector, is chosen as the model’s infrastructure. The DAAPS model is the first to combine the Anchor-free Person Search model with Deformable Attention Mechanism, applied to guide the model adaptively adjust the perceptual. The Deformable Attention Mechanism is used to help the model focus on the critical information and effectively improve the poor accuracy caused by the absence of anchor boxes. The experiment proves the adaptability of the Attention mechanism to the Anchor-free model. Besides, with an improved ResNeXt+ network frame, the DAAPS model selects the Triplet-based Online Instance Matching (TOIM) Loss function to achieve a more precise end-to-end Person Search task. Simulation experiments demonstrate that the proposed model has higher accuracy and better robustness than most Person Search models, reaching 95.0% of mean Average Precision (mAP) and 95.6% of Top-1 on the CUHK-SYSU dataset, 48.6% of mAP and 84.7% of Top-1 on the Person Re-identification in the Wild (PRW) dataset, respectively.