Abstract
With significant increasing of surveillance cameras, the amount of surveillance videos is growing rapidly. Thereby how to automatically and efficiently recognize semantic actions and events in surveillance videos becomes an important problem to be addressed. In this paper, we investigate the state-of-the-art Deep Learning (DL) approaches for human action recognition, and propose an improved two-stream ConvNets architecture for this task. In particular, we propose to use Motion History Image (MHI) as motion expression for training the temporal ConvNet, which achieved impressive results in both accuracy and recognition speed. In our experiment, we conducted an in-depth study to investigate important network options and compared to the latest deep network for action recognition. The detailed evaluation results show the superior ability of our proposed approach, which achieves state-of-the-art in surveillance video context.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
References
Brox, T., Bruhn, A., Papenberg, N., Weickert, J.: High accuracy optical flow estimation based on a theory for warping. In: Pajdla, T., Matas, J.G. (eds.) ECCV 2004. LNCS, vol. 3024, pp. 25–36. Springer, Heidelberg (2004)
UCF-ARG Data Set. http://crcv.ucf.edu/data/UCF-ARG.php. Accessed 10 Nov 2015
Baccouche, M., Mamalet, F., Wolf, C., Garcia, C., Baskurt, A.: Sequential deep learning for human action recognition. In: Salah, A.A., Lepri, B. (eds.) HBU 2011. LNCS, vol. 7065, pp. 29–39. Springer, Heidelberg (2011)
Bilinski, P., Bremond, F.: Statistics of pairwise co-occurring local spatio-temporal features for human action recognition. In: Fusiello, A., Murino, V., Cucchiara, R. (eds.) ECCV 2012 Ws/Demos, Part I. LNCS, vol. 7583, pp. 311–320. Springer, Heidelberg (2012)
Davis, J.W., Bobick, A.E.: The representation and recognition of human movement using temporal templates. In: 1997 IEEE Computer Society Conference on CVPR, pp. 928–934. IEEE (1997)
Dollár, P., Belongie, S., Perona, P.: The fastest pedestrian detector in the west. In: BMVC, vol. 2, p. 7. Citeseer (2010)
Donahue, J., Anne Hendricks, L., Guadarrama, S., Rohrbach, M., Venugopalan, S., Saenko, K., Darrell, T.: Long-term recurrent convolutional networks for visual recognition and description. In: 2015 IEEE Conference on CVPR, pp. 2625–2634 (2015)
Gropley, J.: Top Video Surveillance Trends for 2015. IHS Technology, 1 edn. (2015). https://technology.ihs.com/api/binary/520143
Ji, S., Xu, W., Yang, M., Yu, K.: 3D convolutional neural networks for human action recognition. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 221–231 (2013)
Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., Fei-Fei, L.: Large-scale video classification with convolutional neural networks. In: 2014 IEEE Conference on CVPR, pp. 1725–1732. IEEE (2014)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: NIPS, pp. 1097–1105 (2012)
Laptev, I., Marszałek, M., Schmid, C., Rozenfeld, B.: Learning realistic human actions from movies. In: 2008 IEEE Conference on CVPR, pp. 1–8. IEEE (2008)
Nina, O., Rubiano, C., Shah, M.: Action recognition using ensemble of deep convolutional neural networks (2014)
Ryoo, M.S., Chen, C.-C., Aggarwal, J.K., Roy-Chowdhury, A.: An overview of contest on semantic description of human activities (SDHA) 2010. In: Ünay, D., Çataltepe, Z., Aksoy, S. (eds.) ICPR 2010. LNCS, vol. 6388, pp. 270–285. Springer, Heidelberg (2010)
Simonyan, K., Zisserman, A.: Two-stream convolutional networks for action recognition in videos. In: NIPS, pp. 568–576 (2014)
Wang, L., Xiong, Y., Wang, Z., Qiao, Y.: Towards good practices for very deep two-stream convnets. CoRR abs/1507.02159 (2015)
Wu, Z., Wang, X., Jiang, Y.G., Ye, H., Xue, X.: Modeling spatial-temporal clues in a hybrid deep learning framework for video classification. In: Proceedings of the 23rd ACM MM, pp. 461–470. ACM (2015)
Ye, H., Wu, Z., Zhao, R.W., Wang, X., Jiang, Y.G., Xue, X.: Evaluating two-stream CNN for video classification. In: ICMR 2015, pp. 435–442. ACM (2015)
Ng, J.Y-H., Hausknecht, M., Vijayanarasimhan, S., Vinyals, O., Monga, R., Toderici, G.: Beyond short snippets: Deep networks for video classification. In: 2015 IEEE Conference on CVPR, pp. 4694–4702 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Luo, S., Yang, H., Wang, C., Che, X., Meinel, C. (2016). Action Recognition in Surveillance Video Using ConvNets and Motion History Image. In: Villa, A., Masulli, P., Pons Rivero, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2016. ICANN 2016. Lecture Notes in Computer Science(), vol 9887. Springer, Cham. https://doi.org/10.1007/978-3-319-44781-0_23
Download citation
DOI: https://doi.org/10.1007/978-3-319-44781-0_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-44780-3
Online ISBN: 978-3-319-44781-0
eBook Packages: Computer ScienceComputer Science (R0)