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Hidden Two-Stream Convolutional Networks for Action Recognition

Abstract · Apr 2, 2017 23:39 ·

cs-cv cs-lg cs-mm

Arxiv Abstract

  • Yi Zhu
  • Zhenzhong Lan
  • Shawn Newsam
  • Alexander G. Hauptmann

Analyzing videos of human actions involves understanding the temporal relationships among video frames. CNNs are the current state-of-the-art methods for action recognition in videos. However, the CNN architectures currently being used have difficulty in capturing these relationships. State-of-the-art action recognition approaches rely on traditional local optical flow estimation methods to pre-compute the 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. Our method is 10x faster than a two-stage approach, does not need to cache flow information, and is end-to-end trainable. Experimental results on UCF101 and HMDB51 show that it achieves competitive accuracy with the two-stage approaches.

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