Efficient Two-Stream Motion and Appearance 3D CNNs for Video Classification

Abstract

The video and action classification have extremely evolved by deep neural networks specially with two stream CNN using RGB and optical flow as inputs and they present outstanding performance in terms of video analysis. One of the shortcoming of these methods is handling motion information extraction which is done out side of the CNNs and relatively time consuming also on GPUs. So proposing end-to-end methods which are exploring to learn motion representation, like 3D-CNN can achieve faster and accurate performance. We present some novel deep CNNs using 3D architecture to model actions and motion representation in an efficient way to be accurate and also as fast as real-time. Our new networks learn distinctive models to combine deep motion features into appearance model via learning optical flow features inside the network.

References in corpus (6)

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Cited by in corpus (8)

• A Comprehensive Study of Deep Video Action Recognition
• Temporal Segment Networks for Action Recognition in Videos
• Optical Flow Guided Feature: A Fast and Robust Motion Representation for Video Action Recognition
• ActionFlowNet: Learning Motion Representation for Action Recognition
• Compressed Video Action Recognition with Refined Motion Vector
• Two-Stream Region Convolutional 3D Network for Temporal Activity Detection
• Coupled Recurrent Network (CRN)
• Making a Case for Learning Motion Representations with Phase