Partial AttributeDriven Video Person ReIdentification Person reidentification has gradually become a hot research topic in many fields, such as security, criminal investigation and video analysis. In this paper, we propose a novel feature extraction framework for videobased person reidentification, namely, the partial attributedriven network (PADNet). The proposed method is based on the deeplearning architecture and incorporates the attribute and identity learning of the pedestrian. Existing attribute research always focuses on the feature representation at the globallevel. Unlike them, first, the pedestrian is automatically partitioned to several body parts in our work. Then the pedestrian and hisher body parts are annotated by the global and partial attributes, respectively. Finally, we employ a fourbranch multilabel network to explore the spatialtemporal cues of videos by utilizing these labeled samples. Extensive experiments are conducted on two videobased datasets, including PRID2011 and iLIDSVID. The experimental results demonstrate the superiority and effectiveness of the proposed PADNet over the stateoftheart approaches.

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.