Person re-identification (re-ID) has been gaining in popularity in the research community owing to its numerous applications and growing importance in the surveillance industry.Recent methods often employ partial features for person Oral Irrigators re-ID and offer fine-grained information beneficial for person retrieval.In this paper, we focus on learning improved partial discriminative features using a deep convolutional neural architecture, which includes a pyramid spatial pooling module for efficient person feature representation.Furthermore, we propose a multi-task convolutional network that learns both personal attributes and identities in an end-to-end framework.
Our approach incorporates partial features and global features for identity and attribute prediction, respectively.Experiments on several large-scale person re-ID benchmark data sets demonstrate the accuracy of our approach.For example, we report rank-1 accuracies of 85.37% (+3.
47 %) and 92.81% (+0.51 %) on the DukeMTMC re-ID and Womens tights Market-1501 data sets, respectively.The proposed method shows encouraging improvements compared with the state-of-the-art methods.