Deep Learning Network for Pedestrian Attribute Recognition Based on Dynamic Multi-Task Balancing

Person attribute recognition extracts structured feature of person, which plays a vital role in intelligent video surveillance, such as person re-identification. Firstly, based on R*CNN, we design an end-to-end multi-attribute recognition method based on deep learning network. The region proposal network (RPN) rather than selective search is employed to extract auxiliary regions. An unified network for auxiliary region extraction and attribute recognition is constructed to improve locally attributes. Secondly, in order to enhance the effects of auxiliary region, we split the body ROI into four regions proportionately, such as whole body, head, torso and leg. Each region is in charge of different attributes. And the network splits into four branches at the prediction stage. The primary regions and the second important auxiliary regions are exploited to predict attributes simultaneously. At last, the dynamic adapting loss weighting has the ability to balance the contribution of every task and achieve an optimum performance. That is, the loss weights are inversely correlated with the gradient of loss function, which is to avoiding a certain task is training too fast or too slow. The comparison experiments are elaborated on the Berkeley Attributes of People dataset, an optimum mean average precision (mAP) more than 92% is obtained when compared with state-of-the-art methods. © 2019, Beijing China Science Journal Publishing Co. Ltd. All right reserved.