Motion Feature Network: Fixed Motion Filter for Action Recognition
Myunggi Lee, Seungeui Lee, Sungjoon Son, Gyutae Park, Nojun Kwak
Abstract
Spatio-temporal representations in frame sequences play an important role in the task of action recognition. Previously, a method of using optical flow as a temporal information in combination with a set of RGB images that contain spatial information has shown great performance enhancement in the action recognition tasks. However, it has an expensive computational cost and requires two-stream (RGB and optical flow) framework. In this paper, we propose MFNet (Motion Feature Network) containing motion blocks which make it possible to encode spatio-temporal information between adjacent frames in a unified network that can be trained end-to-end. The motion block can be attached to any existing CNN-based action recognition frameworks with only a small additional cost. We evaluated our network on two of the action recognition datasets (Jester and Something-Something) and achieved competitive performances for both datasets by training the networks from scratch.
BibTeX
@inproceedings{eccv2018_motionfeaturenet,
title = {Motion Feature Network: Fixed Motion Filter for Action Recognition},
author = {Myunggi Lee and Seungeui Lee and Sungjoon Son and Gyutae Park and Nojun Kwak},
booktitle = {ECCV 2018},
year = {2018}
}