Richly Annotated Pedestrian
The Richly Annotated Pedestrian (RAP) dataset is a dataset for pedestrian attribute recognition. It contains 41,585 images collected from indoor surveillance cameras. Each image is annotated with 72 attributes, while only 51 binary attributes with the positive ratio above 1% are selected for evaluation. There are 33,268 images for the training set and 8,317 for testing.
Source: Localization Guided Learning for Pedestrian Attribute Recognition
Image Source: http://www.rapdataset.com/rapv1.html
Variants: RAP
This dataset is used in 1 benchmark:
| Task | Model | Paper | Date |
|---|---|---|---|
| Pedestrian Attribute Recognition | Attribute-Specific Localization | Improving Pedestrian Attribute Recognition With … | 2019-10-10 |
| Pedestrian Attribute Recognition | HP-net | HydraPlus-Net: Attentive Deep Features for … | 2017-09-28 |
Recent papers with results on this dataset: