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

TL;DR

This paper introduces efficient 3D CNN architectures that integrate motion and appearance features end-to-end for faster, more accurate video classification, addressing limitations of traditional two-stream methods.

Contribution

The paper proposes novel 3D CNN models that learn motion representations within the network, enabling real-time performance and improved accuracy in video classification.

Findings

Achieved faster, real-time video classification with integrated motion and appearance features.

Demonstrated improved accuracy over traditional two-stream CNNs.

Presented models that learn optical flow features internally for better motion understanding.

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.