Search video action proposal with recurrent and static YOLO

Abstract

In this paper, we propose a new approach for searching action proposals in unconstrained videos. Our method first produces snippet action proposals by combining state-of-the-art YOLO detector (Static YOLO) and our regression based RNN detector (Recurrent YOLO). Then, these short action proposals are integrated to form final action proposals by solving two-pass dynamic programming which maximizes actioness score and temporal smoothness concurrently. Our experimental comparison with other state-of-the-arts on challenging UCF101 dataset shows that our method advances state-of-the-art proposal generation performance while maintaining low computational cost.