[1] Herath S, Harandi M, Porikli F. Going deeper into action recognition: A survey[J]. Image and Vision Computing, 60, 4-21(2017). http://www.sciencedirect.com/science/article/pii/S0262885617300343

[2] Ma M, Li Y B. Multi-level image sequences and convolutional neural networks based human action recognition method[J]. Journal of Jilin University Engineering and Technology Edition, 47, 1244-1252(2017).

[3] Subetha T, Chitrakala S. A survey on human activity recognition from videos. [C]∥Proceedings of IEEE International Conference on Information Communication and Embedded Systems, 1-7(2016).

[4] Karpathy A, Toderici G, Shetty S et al. Large-scale video classification with convolutional neural networks. [C]∥Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 1725-1732(2014).

[5] Simonyan K, Zisserman A. Two-stream convolutional networks for action recognition in videos[J]. Advances in Neural Information Processing Systems, 1, 568-576(2014). http://dl.acm.org/citation.cfm?id=2968890

[6] Tran D, Bourdev L, Fergus R et al. Learning spatiotemporal features with 3D convolutional networks. [C]∥Proceedings of IEEE International Conference on Computer Vision, 4489-4497(2015).

[7] Donahue J, Hendricks L A, Rohrbach M et al. Long-term recurrent convolutional networks for visual recognition and description[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 677-691(2017). http://www.ncbi.nlm.nih.gov/pubmed/27608449

[8] Wang L M, Xiong Y J, Wang Z et al. Temporal segment networks: Towards good practices for deep action recognition[J]. ACM Transactions on Information Systems, 22, 20-36(2016).

[9] Varol G, Laptev I, Schmid C. Long-term temporal convolutions for action recognition[J]. IEEE Trans Pattern Analysis and Machine Intelligence, 40, 1510-1517(2018). http://doi.ieeecomputersociety.org/10.1109/TPAMI.2017.2712608

[10] Bilen H, Fernando B, Gavves E et al. Dynamic image networks for action recognition. [C]∥Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 3034-3042(2016).

[11] Lin S Z, Zheng Y, Lu X F et al. Adaptive tracking algorithm for aerial small targets based on multi-domain convolutional neural networks and autoregression model[J]. Acta Optica Sinica, 37, 1215006(2017).

[12] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. [C]∥Proceedings of International Conference on Learning Representations, 1-14(2015).

[13] Qu L, Wang K R, Chen L L et al. Fast road detection based on RGBD images and convolutional neural network[J]. Acta Optica Sinica, 37, 1010003(2017).

[14] Feichtenhofer C, Pinz A, Wildes R P. Spatiotemporal residual networks for video action recognition. [C]∥Proceedings of Neural Information Processing Systems, 3468-3476(2016).

[15] Wang H, Schmid C. Action recognition with improved trajectories. [C]∥Proceedings of IEEE International Conference on Computer Vision, 3551-3558(2013).

[16] Peng X J, Wang L M, Wang X X et al. Bag of visual words and fusion methods for action recognition: Comprehensive study and good practice[J]. Computer Vision and Image Understanding, 150, 109-125(2016). http://www.sciencedirect.com/science/article/pii/S1077314216300091

[17] Wang L M, Qiao Y, Tang X O. MoFAP: A multi-level representation for action recognition[J]. International Journal of Computer Vision, 119, 254-271(2016). http://dl.acm.org/citation.cfm?id=2979772"

[18] [18] Zha SX, LuisierF, AndrewsW, et al. Exploiting image-trained CNN architectures for unconstrained video classification[J]. arXiv preprint arXiv:1503. 04144, 2015.

[19] Wang L M, Qiao Y, Tang X O. Action recognition with trajectory-pooled deep-convolutional descriptors. [C]∥Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 4305-4314(2015).

[20] Carreira J, Zisserman A. Quo vadis, action recognition? A new model and the kinetics dataset[J]. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 4724-4733(2017). http://ieeexplore.ieee.org/document/8099985/