Pedestrian Attribute Recognition and Attributed-based Person Retrieval Challenge

 We cordially invite you to participate in our WACV'23 Pedestrian
 Attribute Recognition and Attributed-based Person Retrieval Challenge

Challenge description: The challenge will use an extension of the UPAR
Dataset, which consists of images of pedestrians annotated for 40
binary attributes. For deployment and long-term use of
machine-learning algorithms in a surveillance context, the algorithms
must be robust to domain gaps that occur when the environment
changes. This challenge aims to spotlight the problem of domain gaps
in a real-world surveillance context and highlight the challenges and
limitations of existing methods to provide a direction for future
research. It will be divided in two competition tracks:

    Track 1: Pedestrian Attribute Recognition: The task is to train an
    attribute classifier that accurately predicts persons' semantic
    attributes, such as age or clothing information, under domain

    Track 2: Attribute-based Person Retrieval: Attribute-based person
    retrieval aims to find persons in a huge database of images called
    gallery that match a specific attribute description. The goal of
    this track is to develop an approach that takes binary attribute
    queries and gallery images as input and ranks the images according
    to their similarity to the query.

Challenge webpage: 

Tentative Schedule:

    Start of the Challenge (development phase): Sep 19, 2022

    Start of test phase: Oct 17, 2022

    End of the Challenge: Oct 31, 2022

    Release of final results: Nov 10, 2022