Large-scale video classification with convolutional neural networks

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn , Issue , 2014, Pages 1725-1732

  Karpathy, Andrej ^a,b   Toderici, George ^a   Shetty, Sanketh ^a   Leung, Thomas ^a   Sukthankar, Rahul ^a   Li, Fei Fei ^b  

^a GOOGLE INC   (United States)

^b Stanford University   (United States)

Author keywords

action; classification; convolutional; dataset; large scale; network; neural; recognition; sports; video

Indexed keywords

CLASSIFICATION (OF INFORMATION); CONVOLUTION; IMAGE RECOGNITION; LARGE DATASET; NETWORKS (CIRCUITS); SPORTS;

ACTION; CONVOLUTIONAL; DATASET; LARGE-SCALE; NEURAL; RECOGNITION; VIDEO;

CONVOLUTIONAL NEURAL NETWORKS;

EID: 84911364368     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2014.223     Document Type: Conference Paper

References (28)

1. M. Baccouche, F. Mamalet, C. Wolf, C. Garcia, and A. Baskurt. Sequential deep learning for human action recognition. In Human Behavior Understanding, pages 29-39. Springer, 2011.
2. D. Ciresan, A. Giusti, J. Schmidhuber, et al. Deep neural networks segment neuronal membranes in electron microscopy images. In NIPS, 2012.
3. L. N. Clement Farabet, Camille Couprie and Y. LeCun. Learning hierarchical features for scene labeling. PAMI, 35(8), 2013.
4. C. Couprie, C. Farabet, L. Najman, and Y. LeCun. Indoor semantic segmentation using depth information. Internatinal Conference on Learning Representation, 2013.
5. N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In CVPR, volume 1, 2005.
6. J. Dean, G. Corrado, R. Monga, K. Chen, M. Devin, Q. V. Le, M. Z. Mao, M. Ranzato, A. Senior, P. Tucker, K. Yang, and A. Y. Ng. Large scale distributed deep networks. In NIPS, 2012.
7. J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. FeiFei. Imagenet: A large-scale hierarchical image database. In CVPR, 2009.
8. P. Dollár, V. Rabaud, G. Cottrell, and S. Belongie. Behavior recognition via sparse spatio-temporal features. In International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, 2005.
9. R. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In CVPR, 2014.
10. S. Ji, W. Xu, M. Yang, and K. Yu. 3D convolutional neural networks for human action recognition. PAMI, 35(1):221-231, 2013.
11. A. Krizhevsky, I. Sutskever, and G. Hinton. Imagenet classification with deep convolutional neural networks. In NIPS, 2012.
12. I. Laptev. On space-time interest points. IJCV, 64(2-3):107-123, 2005.
13. I. Laptev, M. Marszalek, C. Schmid, and B. Rozenfeld. Learning realistic human actions from movies. In CVPR, 2008.
14. Q. V. Le, W. Y. Zou, S. Y. Yeung, and A. Y. Ng. Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis. In CVPR, 2011.
15. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278-2324, 1998.
16. J. Liu, J. Luo, and M. Shah. Recognizing realistic actions from videos "in the wild". In CVPR, 2009.
17. J. C. Niebles, C.-W. Chen, and L. Fei-Fei. Modeling temporal structure of decomposable motion segments for activity classification. In ECCV, pages 392-405. Springer, 2010.
18. A. S. Razavian, H. Azizpour, J. Sullivan, and S. Carls-son. CNN features off-the-shelf: an astounding baseline for recognition. arXiv preprint arXiv:1403.6382, 2014.
19. P. Sermanet, S. Chintala, and Y. LeCun. Convolutional neural networks applied to house numbers digit classification. In ICPR, 2012.
20. P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. LeCun. OverFeat: Integrated recognition, localization and detection using convolutional networks. arXiv preprint arXiv:1312.6229, 2013.
21. J. Sivic and A. Zisserman. Video Google: A text retrieval approach to object matching in videos. In ICCV, 2003.
22. K. Soomro, A. R. Zamir, and M. Shah. UCF101: A dataset of 101 human actions classes from videos in the wild. arXiv preprint arXiv:1212.0402, 2012.
23. G. W. Taylor, R. Fergus, Y. LeCun, and C. Bregler. Con-volutional learning of spatio-temporal features. In ECCV. Springer, 2010.
24. M. Varma and A. Zisserman. A statistical approach to texture classification from single images. IJCV, 62(1-2):61-81, 2005.
25. H. Wang, A. Klaser, C. Schmid, and C.-L. Liu. Action recognition by dense trajectories. In CVPR. IEEE, 2011.
26. H. Wang, M. M. Ullah, A. Klaser, I. Laptev, C. Schmid, et al. Evaluation of local spatio-temporal features for action recognition. In BMVC, 2009.
27. W. Yang and G. Toderici. Discriminative tag learning on youtube videos with latent sub-tags. In CVPR, 2011.
28. M. D. Zeiler and R. Fergus. Visualizing and understanding convolutional neural networks. arXiv preprint arXiv:1311.2901, 2013.