Abstract
Abnormal event detection in public scenes is very important in recent society. In this paper, a method for global anomaly detection in video surveillance is proposed, which is based on a deep prediction neural network. The deep prediction neural network is built on the Convolutional Neural Network (CNN) and a variant of the Recurrent Neural Network (RNN)-Long Short-Term Memory (LSTM). Especially, the feature of a frame is the output of CNN, which is instead of the hand-crafted feature. First, the feature of a short video clip is obtained through CNN. Second, the predicted feature of the next frame can be gained by LSTM. Finally, the prediction error is introduced to detect that a frame is abnormal or not after the feature of the frame is achieved. Experimental results of global abnormal event detection show the effectiveness of our deep prediction neural network. Comparing with state-of-the-art methods, the model we proposed obtains superior detection results.
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
References
Thida, M., Yong, Y.L., Climent-Pérez, P., Eng, H.-l., Remagnino, P.: A literature review on video analytics of crowded scenes. In: Atrey, P.K., Kankanhalli, M.S., Cavallaro, A. (eds.) Intelligent Multimedia Surveillance, pp. 17–36. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-41512-8_2
Sjarif, N.N.A., Shamsuddin, S.M., Hashim, S.Z.: Detection of abnormal behaviors in crowd scene: a review. Int. J. Adv. Soft Comput. Appl. 4(1), 1–33 (2012)
Kosmopoulos, D., Chatzis, S.P.: Robust visual behavior recognition. IEEE Signal Process. Mag. 27(5), 34–45 (2010)
Mehran, R., Oyama, A., Shah, M.: Abnormal crowd behavior detection using social force model. In: IEEE Conference on Computer Vision and Pattern Recognition, Miami, pp. 935–942. IEEE (2009)
Amraee, S., Vafaei, A., Jamshidi, K., Adibi, P.: Anomaly detection and localization in crowded scenes using connected component analysis. Multimed. Tools Appl. 77(12), 14767–14782 (2018)
Tziakos, I., Cavallaro, A., Xu, L.Q.: Local abnormality detection in video using subspace learning. In: 7th IEEE International Conference on Advanced Video and Signal Based Surveillance, Boston, pp. 519–525. IEEE (2010)
Lee, D.G., Suk, H.I., Park, S.K., Lee, S.W.: Motion influence map for unusual human activity detection and localization in crowded scenes. IEEE Trans. Circuits Syst. Video Technol. 25(10), 1612–1623 (2015)
Patil, N., Biswas, P.K.: Global abnormal events detection in surveillance video—a hierarchical approach. In: Sixth International Symposium on Embedded Computing and System Design, Patna, pp. 217–222. IEEE (2016)
Wang, T., Snoussi, H.: Detection of abnormal visual events via global optical flow orientation histogram. IEEE Trans. Inf. Forensics Secur. 9(6), 988–998 (2014)
Zhang, Y., Lu, H., Zhang, L., Ruan, X.: Combining motion and appearance cues for anomaly detection. Pattern Recognit. 51, 443–452 (2016)
Cong, Y., Yuan, J., Liu, J.: Sparse reconstruction cost for abnormal event detection. In: IEEE Conference on Computer Vision and Pattern Recognition, Colorado, pp. 3449–3456. IEEE (2011)
Li, A., Miao, Z., Cen, Y., Cen, Y.: Anomaly detection using sparse reconstruction in crowded scenes. Multimed. Tools Appl. 76(24), 26249–26271 (2017)
Tran, H.T., Hogg, D.: Anomaly detection using a convolutional winner-take-all autoencoder. In: Proceedings of the British Machine Vision Conference. British Machine Vision Association, London (2017)
Chalapathy, R., Chawla, S.: Deep learning for anomaly detection: a survey. Eprint Arxiv (2019)
Ravanbakhsh, M., Nabi, M., Mousavi, H., Sangineto, E., Sebe, N.: Plug-and-play cnn for crowd motion analysis: an application in abnormal event detection. In: IEEE Winter Conference on Applications of Computer Vision, Lake Tahoe, pp. 1689–1698. IEEE (2018)
Hasan, M., Choi, J., Neumann, J., Roy-Chowdhury, A.K., Davis, L.S.: Learning temporal regularity in video sequences. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, pp. 733–742. IEEE (2016)
Luo, W., Liu, W., Gao, S.: Remembering history with convolutional LSTM for anomaly detection. In: IEEE International Conference on Multimedia and Expo, Hong Kong, pp. 439–444. IEEE (2017)
Sabokrou, M., et al.: AVID: adversarial visual irregularity detection. In: Jawahar, C.V., Li, H., Mori, G., Schindler, K. (eds.) ACCV 2018. LNCS, vol. 11366, pp. 488–505. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20876-9_31
Sabokrou, M., Fayyaz, M., Fathy, M., Moayed, Z., Klette, R.: Deep-Anomaly: fully convolutional neural network for fast anomaly detection in crowded scenes. Comput. Vis. Image Underst. 172, 88–97 (2018)
Liu, W., Luo, W., Lian, D., Gao, S.: Future frame prediction for anomaly detection–a new baseline. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, pp. 6536–6545. IEEE (2018)
Wang, S., Zhu, E., Yin, J., Porikli, F.: Video anomaly detection and localization by local motion based joint video representation and OCELM. Neurocomputing 277, 161–175 (2018)
Sharif Razavian, A., Azizpour, H., Sullivan, J., Carlsson, S.: CNN features off-the-shelf: an astounding baseline for recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Columbus, pp. 806–813. IEEE (2014)
Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015)
Xingjian, S.H.I., Chen, Z., Wang, H., Yeung, D.Y., Wong, W.K., Woo, W.C.: Convolutional LSTM network: a machine learning approach for precipitation nowcasting. In: Advances in Neural Information Processing Systems, Montréal, pp. 802–810 (2015)
Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: LSTM: A search space odyssey. IEEE Trans. Neural Netw. Learn. Syst. 28(10), 2222–2232 (2016)
UMN: Unusual crowd activity dataset of University of Minnesota, department of computer science and engineering (2006). http://mha.cs.umn.edu/movies/crowd-activity-all.avi
Acknowledgments
This work is supported by the National Key Technology R&D Program of China (no. 2012BAH01F03), NSFC (nos. 61572067,61872034, 61672089, 61273274, and 61572064), the Science and Technology Program of Guangzhou (201804010271), the Natural Science Foundation of Guizhou Province ([2019]1064).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Li, A., Miao, Z., Cen, Y., Mladenovic, V., Liang, L., Zheng, X. (2019). Global Anomaly Detection Based on a Deep Prediction Neural Network. In: Milošević, D., Tang, Y., Zu, Q. (eds) Human Centered Computing. HCC 2019. Lecture Notes in Computer Science(), vol 11956. Springer, Cham. https://doi.org/10.1007/978-3-030-37429-7_21
Download citation
DOI: https://doi.org/10.1007/978-3-030-37429-7_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-37428-0
Online ISBN: 978-3-030-37429-7
eBook Packages: Computer ScienceComputer Science (R0)