We collect a Richly Annotated Pedestrian (RAP) dataset from multi-camera surveillance scenarios for pedestrian attribute analysis. The 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 is current largest pedestrian attribute dataset, which is expected to promote the study of large-sclae attribute recognition systems.
Pedestrian retrieval with various types of queries, e.g. a set of attributes or a portrait photo, has a great application potential in large-scale intelligent surveillance systems. This paper presents a Richly Annotated Pedestrian (RAP) dataset v2.0 for the pedestrian retrieval application, which is collected from uncontrolled multi-camera surveillance scenarios. RAP dataset v2.0 is an extended version of RAP dataset v1.0. RAP dataset v1.0 only contains 41585 attribute annotated pedestrian images. As an extension vision, RAP dataset v2.0 adds identity annotations for a part of v1.0 and collects more attribute annotated pedestrian images as well. In total, RAP dataset v2.0 has 84,928 attribute annotated pedestrian samples, and 26,638 of them are also identity annotated. The same as v1.0, each of pedestrian images in v2.0 is also annotated with viewpoints, occlusions, and body parts information, besides of 72 attributes. Based on this dataset, quantitative analysis are made by an amount of state-of-the-art algorithms on three tasks, i.e., pedestrian attribute recognition, attribute-based person retrieval and image-based person retrieval (person re-identification), to build a high-quality person retrieval benchmark. We hope the RAP dataset v2.0 can promote the research of person retrieval in real scenarios.
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If you use RAP datasets in your research, please kindly cite our work as:
@article{li2016richly,
title={A Richly Annotated Dataset for Pedestrian Attribute Recognition},
author={Li, Dangwei and Zhang, Zhang and Chen, Xiaotang and Ling, Haibin and Huang, Kaiqi},
journal={arXiv preprint arXiv:1603.07054},
year={2016}
}
@article{li2019richly,
title={A richly annotated pedestrian dataset for person retrieval in real surveillance scenarios},
author={Li, Dangwei and Zhang, Zhang and Chen, Xiaotang and Huang, Kaiqi},
journal={IEEE transactions on image processing},
volume={28},
number={4},
pages={1575--1590},
year={2019},
publisher={IEEE}
}