  In this paper, we aim to improve the dataset foundation for pedestrian
attribute recognition in real surveillance scenarios. Recognition of human
attributes, such as gender, and clothes types, has great prospects in real
applications. However, the development of suitable benchmark datasets for
attribute recognition remains lagged behind. Existing human attribute datasets
are collected from various sources or an integration of pedestrian
re-identification datasets. Such heterogeneous collection poses a big challenge
on developing high quality fine-grained attribute recognition algorithms.
Furthermore, human attribute recognition are generally severely affected by
environmental or contextual factors, such as viewpoints, occlusions and body
parts, while existing attribute datasets barely care about them. To tackle
these problems, we build a Richly Annotated Pedestrian (RAP) dataset from real
multi-camera surveillance scenarios with long term collection, where data
samples are annotated with not only fine-grained human attributes but also
environmental and contextual factors. RAP has in total 41,585 pedestrian
samples, each of which is annotated with 72 attributes as well as viewpoints,
occlusions, body parts information. To our knowledge, the RAP dataset is the
largest pedestrian attribute dataset, which is expected to greatly promote the
study of large-scale attribute recognition systems. Furthermore, we empirically
analyze the effects of different environmental and contextual factors on
pedestrian attribute recognition. Experimental results demonstrate that
viewpoints, occlusions and body parts information could assist attribute
recognition a lot in real applications.
