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In this paper, we aim to improve the dataset foundation for pedestrianattribute recognition in real surveillance scenarios. Recognition of humanattributes, such as gender, and clothes types, has great prospects in realapplications. However, the development of suitable benchmark datasets forattribute recognition remains lagged behind. Existing human attribute datasetsare collected from various sources or an integration of pedestrianre-identification datasets. Such heterogeneous collection poses a big challengeon developing high quality fine-grained attribute recognition algorithms.Furthermore, human attribute recognition are generally severely affected byenvironmental or contextual factors, such as viewpoints, occlusions and bodyparts, while existing attribute datasets barely care about them. To tacklethese problems, we build a Richly Annotated Pedestrian (RAP) dataset from realmulti-camera surveillance scenarios with long term collection, where datasamples are annotated with not only fine-grained human attributes but alsoenvironmental and contextual factors. RAP has in total 41,585 pedestriansamples, each of which is annotated with 72 attributes as well as viewpoints,occlusions, body parts information. To our knowledge, the RAP dataset is thelargest pedestrian attribute dataset, which is expected to greatly promote thestudy of large-scale attribute recognition systems. Furthermore, we empiricallyanalyze the effects of different environmental and contextual factors onpedestrian attribute recognition. Experimental results demonstrate thatviewpoints, occlusions and body parts information could assist attributerecognition a lot in real applications.

A Richly Annotated Dataset for Pedestrian Attribute Recognition