Deep Multi-Task Network for Learning Person Identity and Attributes

Deep Multi-Task Network for Learning Person Identity and Attributes

Blog Article

Person re-identification (re-ID) has been gaining in popularity in the research community owing to its numerous 2006 nissan altima radio applications and growing importance in the surveillance industry.Recent methods often employ partial features for person 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 pet calming peanut butter 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 Market-1501 data sets, respectively.The proposed method shows encouraging improvements compared with the state-of-the-art methods.