A Richly Annotated Pedestrian Dataset for Person Retrieval in Real Surveillance Scenarios

You are here

Top Reasons to Join SPS Today!

1. IEEE Signal Processing Magazine
2. Signal Processing Digital Library*
3. Inside Signal Processing Newsletter
4. SPS Resource Center
5. Career advancement & recognition
6. Discounts on conferences and publications
7. Professional networking
8. Communities for students, young professionals, and women
9. Volunteer opportunities
10. Coming soon! PDH/CEU credits
Click here to learn more.

A Richly Annotated Pedestrian Dataset for Person Retrieval in Real Surveillance Scenarios

By: 
Dangwei Li, Zhang Zhang, Xiaotang Chen, Kaiqi Huang

Retrieving specific persons with various types of queries, e.g., a set of attributes or a portrait photo has great application potential in large-scale intelligent surveillance systems. In this paper, we propose a richly annotated pedestrian (RAP) dataset which serves as a unified benchmark for both attribute-based and image-based person retrieval in real surveillance scenarios. Typically, previous datasets have three improvable aspects, including limited data scale and annotation types, heterogeneous data source, and controlled scenarios. Differently, RAP is a large-scale dataset which contains 84928 images with 72 types of attributes and additional tags of viewpoint, occlusion, body parts, and 2589 person identities. It is collected in the real uncontrolled scene and has complex visual variations in pedestrian samples due to the change of viewpoints, pedestrian postures, and cloth appearance. Towards a high-quality person retrieval benchmark, an amount of state-of-the-art algorithms on pedestrian attribute recognition and person re-identification (ReID), are performed for quantitative analysis with three evaluation tasks, i.e., attribute recognition, attribute-based and image-based person retrieval, where a new instance-based metric is proposed to measure the dependency of the prediction of multiple attributes. Finally, some interesting problems, e.g., the joint feature learning of attribute recognition and ReID, and the problem of cross-day person ReID, are explored to show the challenges and future directions in person retrieval.

SPS on Facebook

SPS on Twitter

SPS Videos


Signal Processing in Home Assistants

 


Multimedia Forensics


Careers in Signal Processing             

 


Under the Radar