2020 Sequestered Data Evaluation for Known Activities in Extended Video: Summary and Results

Afzal Godil, Yooyoung Lee, Jon Fiscus, Andrew Delgado, Eliot Godard, Baptiste Chocot, Lukas Diduch, Jim Golden, Jesse Zhang; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Workshops, 2021, pp. 51-59

This paper presents a summary and results for the ActEV'20 SDL (Activities in Extended Video Sequestered Data Leaderboard) challenge that was held under the CVPR'20 ActivityNet workshop. The primary goal of the challenge was to provide an impetus for advancing research and capabilities in the field of human activity detection in untrimmed multi-camera videos. Advancements in activity detection will help with a wide range of public safety applications. The challenge was administered by the National Institute of Standards and Technology (NIST), where anyone could submit their system which run on sequestered data with the resulting score posted to a public leaderboard. Ten teams submitted their systems for the ActEV'20 SDL competition on the Multiview Extended Video with Activities (MEVA) test set with 37 target activities. The performance metric for the leaderboard ranking is the partial, normalized Area Under the Detection Error Tradeoff (DET) curve (nAUDC). The top rank on activity detection was by UCF at 37%, followed by CMU at 39% and OPPO at 41%.