Attentional Part-based Network for Person Re-identification

Yinsong Xu, Zhuqing Jiang, Aidong Men, Jiangbo Pei, Guodong Ju, Bo Yang

Abstract: Part-based network is an effective method to improve performance in person re-identification (re-ID). Most existing methods assume the availability of well-aligned person bounding box images as model input. However, automatic detection in some datasets causes misalignment which negatively affects the performance. In this work, we propose an Attentional Part-based CNN (AP-CNN) model which combines learning partial features and attention selection. First, we partition feature map into several horizontal stripes. Second, we use attention selection in each stripe to align the pedestrian images. Inside, we introduce a free-parameter attention model with skip-layer connection which maximizes the complementary information of different levels without increasing the complexity of network. Results on four datasets validate the competitiveness of AP-CNN over the state-of-the-art achieving Rank-1 accuracy of 94.4% on Market-1501, 87.3% on DukeMTMC-ReID, 73.7% on CUHK03-labeled and 72.6% on CUHK03-detected.