Attentional Partbased Network for Person Reidentification Partbased network is an effective method to improve performance in person reidentification (reID). Most existing methods assume the availability of wellaligned 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 Partbased CNN (APCNN) 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 freeparameter attention model with skiplayer connection which maximizes the complementary information of different levels without increasing the complexity of network. Results on four datasets validate the competitiveness of APCNN over the stateoftheart achieving Rank1 accuracy of 94.4% on Market1501, 87.3% on DukeMTMCReID, 73.7% on CUHK03labeled and 72.6% on CUHK03detected.

Partguided Network for Pedestrian Attribute Recognition Pedestrian attribute recognition, which can benefit other tasks such as person reidentification 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 multilabel 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 Partguided Network (PNet), which guides the refined convolutional feature maps to capture different location information for the attributes related to different body parts. The partguided attention module employs the pixlevel classification to produce attention maps which can be interpreted as the probability of each pixel belonging to the 6 predefined body parts. Experimental results demonstrate that the proposed network gives superior performances compared to the stateoftheart techniques.