Hidden Two-Stream Convolutional Networks for Action Recognition
Abstract
Analyzing videos of human actions involves understanding the temporal relationships among video frames. State-of-the-art action recognition approaches rely on traditional optical flow estimation methods to pre-compute motion information for CNNs. Such a two-stage approach is computationally expensive, storage demanding, and not end-to-end trainable. In this paper, we present a novel CNN architecture that implicitly captures motion information between adjacent frames. We name our approach hidden two-stream CNNs because it only takes raw video frames as input and directly predicts action classes without explicitly computing optical flow. Our end-to-end approach is 10x faster than its two-stage baseline. Experimental results on four challenging action recognition datasets: UCF101, HMDB51, THUMOS14 and ActivityNet v1.2 show that our approach significantly outperforms the previous best real-time approaches.
Cite
@article{arxiv.1704.00389,
title = {Hidden Two-Stream Convolutional Networks for Action Recognition},
author = {Yi Zhu and Zhenzhong Lan and Shawn Newsam and Alexander G. Hauptmann},
journal= {arXiv preprint arXiv:1704.00389},
year = {2018}
}
Comments
Accepted at ACCV 2018, camera ready. Code available at https://github.com/bryanyzhu/Hidden-Two-Stream