Title: Tube Convolutional Neural Network (T-CNN) for Action Detection in Videos,
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.