Part-guided network for pedestrian attribute recognition

Pedestrian attribute recognition, which can benefit other tasks such as person re-identification and pedestrian retrieval, is very important in video surveillance related tasks. In this paper, we observe that the existing methods tackle this problem from the perspective of multi-label classification without considering the spatial location constraints, which means that the attributes tend to be recognized at certain body parts. Based on that, we propose a novel Part-guided Network (P-Net), which guides the refined convolutional feature maps to capture different location information for the attributes related to different body parts. The part-guided attention module employs the pix-level classification to produce attention maps which can be interpreted as the probability of each pixel belonging to the 6 pre-defined body parts. Experimental results demonstrate that the proposed network gives superior performances compared to the state-of-The-Art techniques.