Скачать или смотреть UPAR Challenge: Pedestrian Attribute Recognition and Attributed-based Person Retrieval - Dataset, D

Authors: Cormier, Mickael*; Specker, Andreas; Jacques Junior, Julio C. S.; Florin, Lucas; Metzler, Jürgen; Moeslund, Thomas B.; Nasrollahi, Kamal; Escalera, Sergio; Beyerer, Jürgen Description: In civilian video security monitoring, retrieving and tracking a person of interest often rely on witness testimony and their appearance description. Deployed systems rely on a large amount of annotated training data and are expected to show consistent performance in diverse areas and generalize well between diverse settings w.r.t. different viewpoints, illumination, resolution, occlusions, and poses for indoor and outdoor scenes. However, for such generalization, the system would require a large amount of various annotated data for training and evaluation. The WACV 2023 Pedestrian Attribute Recognition and Attributed-based Person Retrieval Challenge (UPAR-Challenge) aimed to spotlight the problem of domain gaps in a real-world surveillance context and highlight the challenges and limitations of existing methods.
The UPAR dataset, composed of 40 important binary attributes over 12 attribute categories across four datasets, was extended with data captured from a low-flying UAV from the P-DESTRE dataset. To this aim, 0.6M additional annotations were manually labeled and validated. Each track evaluated the robustness of the competing methods to domain shifts by training on limited data from a specific domain and evaluating using data from unseen domains. The challenge attracted 41 registered participants, but only one team managed to outperform the baseline on one track, emphasizing the task's difficulty. This work describes the challenge design, the adopted dataset, obtained results, as well as future directions on the topic.