Challenge @ECCV’2022: Concept drift in Object Detection applied to the largest annotated public thermal database
Challenge description: The challenge will use an extension of the LTD Dataset (Nikolov, Ivan Adriyanov, et al. "Seasons in Drift: A Long-Term Thermal Imaging Dataset for Studying Concept Drift." NeurIPS, 2021) which consists of thermal footage that spans multiple seasons. For deployment and long-term use of machine-learning algorithms in a surveillance context it is vital that the algorithm is robust to the concept drift that occurs as the conditions in the outdoor environment changes. This challenge aims to spotlight the problem of concept drift in a surveillance context and highlight the challenges and limitations of existing methods. It will be divided into three competition tracks. Depending on the track chosen the training data will vary, however the validation and testing data will remain the same across all challenges.
Track 1 - Detection at day level: Train on a predefined and single day data and evaluate concept drift across time.
Track 2 - Detection at week level: Train on a predefined and single week data and evaluate concept drift across time.
Track 3 - Detection at month level: Train on a predefined and single month data and evaluate concept drift across time.
Challenge webpage: https://chalearnlap.cvc.uab.cat/challenge/51/description/
Start of the Challenge (development phase): April 25, 2022
Start of test phase: June 17, 2022
End of the Challenge: June 24, 2022
Release of final results: July 1st, 2022
Participants are invited to submit their contributions to the associated ECCV’22 Workshop (https://vap.aau.dk/rws-eccv2022/), independently of their rank position.
ORGANIZATION and CONTACT
Thomas B. Moeslund, Aalborg University, Aalborg, Denmark
Julio C. S. Jacques Junior, Computer Vision Center (CVC), Spain
Anders Skaarup Johansen, Aalborg University, Denmark
Radu Ionescu, University of Bucharest, Romania
Fahad Shahbaz Khan, Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates, and Linköping University, Sweden
Anthony Hoogs, Kitware, USA
Shmuel Peleg, Hebrew University, Israel
Mubarak Shah, University of Central Florida, USA
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