The PEdesTrian Attribute dataset (PETA) is a dataset fore recognizing pedestrian attributes, such as gender and clothing style, at a far distance. It is of interest in video surveillance scenarios where face and body close-shots and hardly available. It consists of 19,000 pedestrian images with 65 attributes (61 binary and 4 multi-class). Those images contain 8705 persons.

The RAP dataset is used for pedestrian attribute recognition. It contains large-scale pedestrian attribute datasets.

PA-100K is a recent-proposed large pedestrian attribute dataset, with 100,000 images in total collected from outdoor surveillance cameras. It is split into 80,000 images for the training set, and 10,000 for the validation set and 10,000 for the test set. This dataset is labeled by 26 binary attributes. The common features existing in both selected dataset is that the images are blurry due to the relatively low resolution and the positive ratio of each binary attribute is low.

Pedestrian attribute recognition at far distance

CAR contains visual attributes for objects in the Cityscapes dataset. For each object in an image, we have a list of attributes that depend on the category of the object. For instance, a vehicle category has a visibility attribute while a pedestrian has an activity attribute (walking, standing, etc.). The objective of this dataset is to ease the development of better algorithms for self-driving vehicles as that requires a complete understanding of the entire scene with all of its details including attributes of all objects. We chose Cityscapes as it already contains different types of useful annotations and adding attributes to that will remove a huge burden over developing algorithms with self or semi supervision.