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 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 generation and association of proposals across frames. Also, most of 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 deep network that is able to recognize and localize action based on 3D convolution features. A video is first divided into equal length clips and next for each clip a set of tube proposals are generated 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.
Publication Date
12-22-2017
Publication Title
Proceedings of the IEEE International Conference on Computer Vision
Volume
2017-October
Number of Pages
5823-5832
Document Type
Article; Proceedings Paper
Personal Identifier
scopus
DOI Link
https://doi.org/10.1109/ICCV.2017.620
Copyright Status
Unknown
Socpus ID
85041907672 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/85041907672
STARS Citation
Hou, Rui; Chen, Chen; and Shah, Mubarak, "Tube Convolutional Neural Network (T-Cnn) For Action Detection In Videos" (2017). Scopus Export 2015-2019. 7462.
https://stars.library.ucf.edu/scopus2015/7462