243
Views
0
CrossRef citations to date
0
Altmetric
Research Article

A Dual Stream Model for Activity Recognition: Exploiting Residual- CNN with Transfer Learning

ORCID Icon, , &
Pages 28-38 | Received 22 Jan 2020, Accepted 01 Aug 2020, Published online: 17 Aug 2020

References

  • Andrew Z, Joao C, Karen S, Will K, Brian Z, Chloe H, Sudheendra V, Fabio V, Tim G, Trevor B. 2017. The kinetics human action video dataset. arXiv:1705 06950 [Cs CV]
  • Baccouche M, Mamalet F, Wolf C, Garcia C, Baskurt A. 2011. sequential deep learning for human action recognition. In: Human behavior understanding. p. 29–39.
  • Bobick AF, Davis JW. 2001. The recognition of human movement using temporal templates BT - Pattern analysis and machine intelligence, IEEE transactions on. IEEE Trans Pattern Anal Mach Intell. 23(3):257–267.
  • Chaudhary S, Murala S. 2018. “TSNet: deep network for human action recognition in Hazy videos.” 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Miyazaki, Japan, 2018, 3981–3986.
  • Feichtenhofer C, Pinz A, Zisserman A, “convolutional two-stream network fusion for video action recognition,” 2016.
  • Ji S, Xu W, Yang M, Yu K. 2013. 3D Convolutional neural networks for human action recognition. IEEE Trans Pattern Anal Mach Intell. 35(1):221–231.
  • Karpathy A, Toderici G, Shetty S, Leung T, Sukthankar R, Li FF. 2014. “Large-Scale Video Classification with Convolutional Neural Networks,” 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, 2014, 1725–1732.
  • Ke Q, An S, Bennamoun M, Sohel F, Boussaid F. 2016. SkeletonNet : mining Deep Part Features for 3D Action Recognition. Proc in IEEE Signal Processing Letters,  24(6):731–735, 2017.
  • Kuehne H, Jhuang H, Garrote E, Poggio T, Serre T. 2011. “HMDB: A large video database for human motion recognition.” 2011 International Conference on Computer Vision, Barcelona, 2011, 2556–2563.
  • Laptev I. 2005. On space-time interest points. Int J Comput Vis. 64(2–3):107–123.
  • Li C., Q. Zhong, and D. Xie, 2017. “Skeleton-Based Action Recognition with Convolutional Neural Networks.” July, pp. 597–600.
  • Li -F-F, Dong W, Deng J, Li K, Socher R, Li-Jia L. 2009. ImageNet: A large-scale hierarchical image database. 2009 IEEE Conf Comput Vis Pattern Recognit. 248–255.
  • Liu J, Luo J, Shah M, “Recognizing realistic actions from videos ́in the Wild,” 2009 IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. Work. CVPR Work. vol. 2009 IEEE, Miami, Florida City USA, pp. 1996–2003, 2009.
  • O’Reilly RC, Wyatte D, Herd S, Mingus B, Jilk DJ. 2013. Deep Residual Learning for Image Recognition Kaiming. Front Psychol. 4(APR):428–429.
  • Poppe R. 2010. A survey on vision-based human action recognition. Image Vis Comput. 28(6):976–990.
  • Shahroudy A, Liu J, Ng -T-T, Wang G, “NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis,” in In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Caesars Palace, Las Vegas, Nevada, United States, 2016, pp. 1010–1019.
  • Shamsolmoali P, Zareapoor M, Jiao S. 2020. AMIL : adversarial Multi-instance Learning for Human Pose Estimation. ACM Trans. Multimedia Comput. Commun. Appl. 16(1):1–23.
  • Simonyan K, Zisserman A. 2014. Two-Stream Convolutional Networks for Action Recognition in Videos. In Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 1 (NIPS’14). MIT Press, Cambridge, MA, USA, 568–576.
  • Singh R, Dhillon JK, Kushwaha AKS, Srivastava R. 2018. Depth based enlarged temporal dimension of 3D deep convolutional network for activity recognition. Multimed Tools Appl. 78:30599–30614.
  • Singh R, Khurana R, Kumar A, Kushwaha S, Srivastava R. 2020. Combining CNN streams of dynamic image and depth data for action recognition. Multimedia Systems Volume. 26:313–322.
  • Soomro K, Zamir AR, Shah M, “UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild,” November, 2012.
  • Szegedy C, Wei L, Yangqing J, Pierre S, Scott R, Dragomir A, Dumitru E, Vincent V, Andrew R. 2015. Going deeper with convolutions. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit. 07–12–June:1–9
  • Taylor GW, Fergus R, LeCun Y, Bregler C. 2010. Convolutional learning of spatio-temporal features. Lect Notes Comput Sci (Including Subser Lect Notes Artif Intell Lect Notes Bioinformatics). 6316 LNCS(. PART 6):140–153.
  • Tran D, Bourdev L, Fergus R, Torresani L, Paluri M. 2015. Learning spatiotemporal features with 3D convolutional networks. Proc IEEE Int Conf Comput Vis. 2015(Inter):4489–4497.
  • Varol G, Laptev I, Schmid C. 2016. in IEEE Transactions on Pattern Analysis and Machine Intelligence. arXiv Prepr arXiv1604 04494. 1510–1517.
  • Wang J, Cherian A, Porikli F. 2017. Ordered pooling of optical flow sequences for action recognition. 2017 IEEE Winter Conference on Applications of Computer Vision (WACV), Santa Rosa, CA. 168–176
  • Wang P, Li W, Gao Z, Zhang J, Tang C, Ogunbona PO. 2016. Action Recognition from Depth Maps Using Deep Convolutional Neural Networks. IEEE Trans Human-Machine Syst. 46(4):498–509.
  • Willems G, Tuytelaars T, Van Gool L. 2008. An Efficient Dense and Scale-Invariant Spatio-Temporal Interest Point Detector. Proc Lecture Notes in Computer Science, Springer, Berlin, Heidelberg. IEEE Int Conf Comput Vis. 5503:2923–2932.
  • Zolfaghari M, Oliveira GL, Sedaghat N, Brox T. 2017. Chained Multi-stream Networks Exploiting Pose, Motion, and Appearance for Action Classification and Detection. Proc IEEE Int Conf Comput Vis. 2017–Octob(2):2923–2932.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.