부산대학교 도서관

Appearance information such as clothing and hairstyle can provide rich clues to identify a person in surveillance videos. This paper proposes a Faster R-CNN based multi-task neural network to recognize attributes such as gender, nationality, etc. from clothing of a person. Toward this end, a fine-tuned Faster R-CNN is applied to locate people in images. Then hierarchical features are extracted from these regions to perform attributes recognition. The recognition network and Faster R-CNN share the same weights, and they can be trained in an end-to-end manner. Experiments are conducted on a newly collected and well-labeled image dataset. The experimental results show that our network can locate a human body and identify its attributes efficiently. [ABSTRACT FROM AUTHOR]