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arXiv · 1604.04494

Long-term Temporal Convolutions for Action Recognition

Gül Varol·Ivan Laptev·Cordelia Schmid
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Abstract

Typical human actions last several seconds and exhibit characteristic spatio-temporal structure. Recent methods attempt to capture this structure and learn action representations with convolutional neural networks. Such representations, however, are typically learned at the level of a few video frames failing to model actions at their full temporal extent. In this work we learn video representations using neural networks with long-term temporal convolutions (LTC). We demonstrate that LTC-CNN models with increased temporal extents improve the accuracy of action recognition. We also study the impact of different low-level representations, such as raw values of video pixels and optical flow vector fields and demonstrate the importance of high-quality optical flow estimation for learning accurate action models. We report state-of-the-art results on two challenging benchmarks for human action recognition UCF101 (92.7%) and HMDB51 (67.2%).

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Gül Varol, Ivan Laptev, Cordelia Schmid. 2017-06-02. Long-term Temporal Convolutions for Action Recognition. https://arxiv.org/abs/1604.04494

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