Safe-Net: Solid and Abstract Feature Extraction Network for Pedestrian Attribute Recognition
Daiheng Gao, Zhenzhi Wu, Weihao Zhang · 2019
Pedestrian attribute analysis, which is a vital component in the intelligent video surveillance area, can facilitate person retrieval, searching and indexing. However, the resolution of the surveillance video is relatively low. The ability of neural networks to learn abstract features (age, gender) and solid features (hat, backpack, clothes) on low-resolution images is limited due to the inability to accurately locate the human body and the disturbance of background noise. In this study, we adopt a Semantic Parsing Technique (SPT) as a pedestrian extractor to localize informative regions of human effectively. Experiments are conducted on the RAP, PA-100k and other datasets, we show that our proposed SAFE-Net is capable of capturing abstract features and solid features, and produces competitive performance with the state-of-the-art methods.