Tube Convolutional Neural Network (T-CNN) for Action Detection in Videos

TL;DR

This paper introduces T-CNN, an end-to-end 3D convolutional network that effectively detects and localizes actions in videos by linking tube proposals across clips, outperforming previous methods.

Contribution

The paper presents a novel unified 3D CNN architecture for spatio-temporal action detection that integrates proposal generation and linking in an end-to-end manner.

Findings

T-CNN achieves superior accuracy on multiple video datasets.

The method effectively localizes actions in both trimmed and untrimmed videos.

End-to-end training simplifies the action detection pipeline.

Abstract

Deep learning has been demonstrated to achieve excellent results for image classification and object detection. However, the impact of deep learning on video analysis (e.g. action detection and recognition) has been limited due to complexity of video data and lack of annotations. Previous convolutional neural networks (CNN) based video action detection approaches usually consist of two major steps: frame-level action proposal detection and association of proposals across frames. Also, these methods employ two-stream CNN framework to handle spatial and temporal feature separately. In this paper, we propose an end-to-end deep network called Tube Convolutional Neural Network (T-CNN) for action detection in videos. The proposed architecture is a unified network that is able to recognize and localize action based on 3D convolution features. A video is first divided into equal length clips and for each clip a set of tube proposals are generated next based on 3D Convolutional Network (ConvNet) features. Finally, the tube proposals of different clips are linked together employing network flow and spatio-temporal action detection is performed using these linked video proposals. Extensive experiments on several video datasets demonstrate the superior performance of T-CNN for classifying and localizing actions in both trimmed and untrimmed videos compared to state-of-the-arts.