APT: Action localization Proposals from dense Trajectories

This paper is on action localization in video with the aid of spatio-temporal proposals. To alleviate the computational expensive video segmentation step of existing proposals, we propose bypassing the segmentations completely by generating proposals directly from the dense trajectories used to represent videos during classification. Our Action localization Proposals from dense Trajectories (APT) uses an efficient proposal generation algorithm to handle the high number of trajectories in a video. Our spatio-temporal proposals are faster than current methods and outperform the localization and classification accuracy of current proposals on UCF Sports, UCF 101, and MSR-II video datasets.