PEDESTRIAN ATTRIBUTE RECOGNITION BASED ON MTCNN WITH ONLINE BATCH WEIGHTED LOSS

Due to the large number and huge diversity of attributes, pedestrian attribute recognition in video surveillance scenarios is a challenging task in the field of computer vision. Different from most previous works which only focus on extremely imbalanced attribute distribution problem, a new grouping way of attributes based multi-task convolutional neural network (MTCNN) is put forward, which exploits the spatial correlations among attributes and guarantees some independence of each attribute as well. Meanwhile, we propose a novel online batch weighted loss to narrow the performance differences among attributes and boost the model to gain a higher average recognition accuracy. The whole network can be trained end to end, and experimental results on PETA and RAP datasets show that our method achieves significant performance, comparing with those state-of-the-art methods.