PARNet: A Joint Loss Function and Dynamic Weights Network for Pedestrian Semantic Attributes Recognition of Smart Surveillance Image

Yong Li, Guofeng Tong, Xin Li, Yuebin Wang, Bo Zou, Yujie Liu
2019 Applied Sciences  
The capability for recognizing pedestrian semantic attributes, such as gender, clothes color and other semantic attributes is of practical significance in bank smart surveillance, intelligent transportation and so on. In order to recognize the key multi attributes of pedestrians in indoor and outdoor scenes, this paper proposes a deep network with dynamic weights and joint loss function for pedestrian key attribute recognition. First, a new multi-label and multi-attribute pedestrian dataset,
more » ... estrian dataset, which is named NEU-dataset, is built. Second, we propose a new deep model based on DeepMAR model. The new network develops a loss function, which joins the sigmoid function and the softmax loss to solve the multi-label and multi-attribute problem. Furthermore, the dynamic weight in the loss function is adopted to solve the unbalanced samples problem. The experiment results show that the new attribute recognition method has good generalization performance.
doi:10.3390/app9102027 fatcat:4sgrtobe2vaatp7f2owpxvyhxa