CMU Informedia at TRECVID 2020: Activity Detection with Dense Spatio-Temporal Proposals

NIST

We propose an action recognition system for surveillance scenarios, which wins TRECVID 2020 Activities in Extended Video (ActEV) Challenge with a large advantage of 23.8% ahead the runner up system. Our system develops a dense spatial-temporal proposal generation model which collaborates with the state-of-the-art action classifiers. The proposed system utilizes multiple state-of-the-art modules and is trained on VIRAT Dataset with only released annotations. In this paper, we demonstrate the architecture and algorithms with technique details of the winner system.

@inproceedings{yucmu2020,
  title={ {CMU} Informedia at {TRECVID} 2020: Activity Detection with Dense Spatio-Temporal Proposals},
  author={Yu, Lijun and Qian, Yijun and Liu, Wenhe and Hauptmann, Alexander G.},
  booktitle={TRECVID},
  year={2020}
}