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 shifts.
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: https://chalearnlap.cvc.uab.cat/challenge/52/description/
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
Participants are invited to submit their contributions to the associated 3rd Workshop on Real-World Surveillance: Applications and Challenges (RWS @ WACV2023) (https://vap.aau.dk/rws-wacv2023/), independently of their rank position.
Sergio Escalera, Computer Vision Center (CVC) and University of Barcelona, Spain
Mickael Cormier, Karlsruhe Institute of Technology (KIT), Germany and Fraunhofer IOSB
Kamal Nasrollahi, Milestone Systems and Aalborg University, Denmark
Andreas Specker, Karlsruhe Institute of Technology (KIT), Germany and Fraunhofer IOSB
Julio C. S. Jacques Junior, Computer Vision Center (CVC), Spain
Jürgen Beyerer, Karlsruhe Institute of Technology (KIT), Germany and Fraunhofer IOSB