Modeling Video Evolution for Action Recognition

Basura Fernando, Efstratios Gavves, Jose Oramas M., Amir Ghodrati, Tinne Tuytelaars; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 5378-5387

Abstract


In this paper we present a method to capture video-wide temporal information for action recognition. We postulate that a function capable of ordering the frames of a video temporally (based on the appearance) captures well the evolution of the appearance within the video. We learn such ranking functions per video via a ranking machine and use the parameters of these as a new video representation. The proposed method is easy to interpret and implement, fast to compute and effective in recognizing a wide variety of actions. We perform a large number of evaluations on datasets for generic action recognition (Hollywood2 and HMDB51), fine-grained actions (MPII- cooking activities) and gestures (Chalearn). Results show that the proposed method brings an absolute improvement of 7-10\%, while being compatible with and complementary to further improvements in appearance and local motion based methods.

Related Material


[pdf]
[bibtex]
@InProceedings{Fernando_2015_CVPR,
author = {Fernando, Basura and Gavves, Efstratios and Oramas, Jose M. and Ghodrati, Amir and Tuytelaars, Tinne},
title = {Modeling Video Evolution for Action Recognition},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2015}
}