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3D-TDC: A 3D temporal dilation convolution framework for video action recognition
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DOI：10.1016/j.neucom.2021.03.120.png)
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Abstract
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Video action recognition is a vital area of computer vision. By adding temporal dimension into convolution structure, 3D convolution neural network owns the capacity to extract spatio-temporal features from videos. However, due to computing constraints, it is hard to input the whole video into the convolution network at one time, resulting in a limited temporal receptive field of the network. To address this issue, we propose a novel 3D temporal dilation convolution (3D-TDC) framework, to extract spatio-temporal features of actions from videos. First, we deploy the 3D temporal dilation convolution as the shallow temporal compression layer, enabling an effective capture of spatio-temporal information in a larger time domain with the reduced computational load. Then, an action recognition framework is constructed by integrating two networks with different temporal receptive fields to balance the long-short time difference. We conduct extensive experiments on three widely-used public datasets (UCF-101, HMDB-51, and Kinetics-400) for performance evaluation, and the experimental results demonstrate the effectiveness of our proposed framework in video action recognition with low computational load. (c) 2021 Elsevier B.V. All rights reserved.
Keywords:
3D convolution
Temporal dilation
Action recognition
Temporal compression
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