APT: Action localization proposals from dense trajectories

APT: Action localization proposals from dense trajectories

Gemert, J.C.
Jain, M.
Gati, E.
Snoek, C.G.M.

Electrical Engineering, Mathematics and Computer Science

Intelligent Systems

2015-09-10

This paper is on action localization in video with the aid of spatio-temporal proposals. To alleviate the computational expensive 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) use 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 the UCF Sports, UCF 101, and MSR-II video datasets. Corrected version: we fixed a mistake in our UCF-101 ground truth. Numbers are different; conclusions are unchanged

http://resolver.tudelft.nl/uuid:d29d1fa5-18f4-42fa-a041-2048e0c7d7e0

BMVA Press

Xianghua Xie, Mark W. Jones, and Gary K. L. Tam, editors, Proceedings of the British Machine Vision Conference (BMVC), BMVA Press, September 2015. Swansea, UK, September 7-10, 2015.

Institutional Repository

conference paper

(c) 2015 The Author(s)