Abstract
Anomaly detection in video surveillance is a significant research subject because of its immense use in real-time applications. These days, open spots like hospitals, traffic areas, airports are monitored by video surveillance cameras. Strange occasions in these recordings have alluded to the anomaly. Unsupervised anomaly detection in the video be endowed with many challenges as there is no exact definition of abnormal events. It varies as for various situations. This paper aims to propose an effective unsupervised deep learning framework for video anomaly detection. Raw image sequences are combined with edge image sequences and given as input to the convolutional auto encoder-ConvLSTM model. Experimental evaluation of the proposed work is performed in three different benchmark datasets such as Avenue, UCSD ped1 and UCSD ped2. The proposed method Hybrid Deep Learning framework for Video Anomaly Detection (HDLVAD) reaches better accuracy compared to existing methods. Investigating video streaming in big data is our further research work.
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References
Amraee S, Vafaei A, Jamshidi K, Adibi P (2018) Abnormal event detection in crowded scenes using one-class SVM, vol. 12
Amraee S, Vafaei A, Jamshidi K, Adibi P (2018) Anomaly detection and localization in crowded scenes using connected component analysis. Multimed Tools Appl 77(12):14767–14782
Canny J (1986) A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell (6):679–698
Chaker R, Aghbari ZA, Junejo IN Social network model for crowd anomaly detection and localization. Pattern Recogn 61:266–281
Cheng KW, Chen YT, Fang WH (2015) Video anomaly detection and localization using hierarchical feature representation and Gaussian process regression. pp. 2909–2917
Chong YS, Tay YH (2017) Abnormal event detection in videos using spatiotemporal autoencoder. In: International Symposium on Neural Networks, pp. 189–196
Colque RVHM, Caetano C, Andrade MTL, Schwartz WR (2017) Histograms of optical flow orientation and magnitude and entropy to detect anomalous events in videos. IEEE Transactions on Circuits and Systems for Video Technology 27(3):673–682
Cong Y, Yuan J, Liu J (2013) Abnormal event detection in crowded scenes using sparse representation. Pattern Recogn 46(7):1851–1864
Fang Z, Fei F, Fang Y, Lee C, Xiong N, Shu L, Chen S. (2016) Abnormal event detection in crowded scenes based on deep learning, vol. 75
Feng Y, Yuan Y, Lu X:X (2016) Deep representation for abnormal event detection in crowded scenes. Neurocomputing 219:591–595
Feng Y, Yuan Y, Lu X (2017) Learning deep event models for crowd anomaly detection. Neurocomputing 219:548–556
Haering N, Venetianer PL, Lipton A (2008) The evolution of video surveillance: an overview, vol. 19
Hu X, Huang Y, Duan Q, Ci W, Dai J, Yang H. (2018) Abnormal event detection in crowded scenes using histogram of oriented contextual gradient descriptor, vol. 2018
Huang S, Huang D, Zhou X (2018) Learning multimodal deep representations for crowd anomaly event detection
Ionescu RT, Smeureanu S, Alexe B, Popescu M (2017) Unmasking the abnormal events in video. pp. 2895–2903
Leyva R, Sanchez V, Li CT (2017) Video anomaly detection with compact feature sets for online performance. IEEE Trans Image Process 26(7):3463–3478
Li W, Mahadevan V, Vasconcelos N (2014) Anomaly detection and localization in crowded scenes. IEEE Trans Pattern Anal Mach Intell 36(1):18–32
Li S, Liu C, Yang Y (2018) Anomaly detection based on maximum a posteriori. Pattern Recogn Lett 107:91–97
Li S, Yang Y, Liu C (2018) Anomaly detection based on two global grid motion templates, vol. 60
Lin H, Deng JD, Woodford BJ, Shahi A (2016) Online weighted clustering for real-time abnormal event detection in video surveillance. pp. 536–540
Liu P, Tao Y, Zhao W, Tang X (2017) Abnormal crowd motion detection using double sparse representation. Neurocomputing 269:3–12
Lu C, Shi J, Jia J (2013) Abnormal event detection at 150 fps in matlab. pp. 2720–2727
Ma D, Wang Q, Yuan Y (2014) Anomaly detection in crowd scene via online learning. p. 158. ACM
Masci J, Meier U, Ciresan D, Schmidhuber J. (2011) Stacked convolutional auto-encoders for hierarchical feature extraction
Narasimhan MG, Kamath S (2017) Dynamic video anomaly detection and localization using sparse denoising autoencoders
Piciarelli C, Micheloni C, Foresti GL (2008) Trajectory-based anomalous event detection. IEEE Transactions on 18(11):1544–1554
Ravanbakhsh M, Sangineto E, Nabi M, Sebe N (2019) Training adversarial discriminators for cross-channel abnormal event detection in crowds. IEEE
Ribeiro M, Lazzaretti AE, Lopes HS (2018) A study of deep convolutional auto-encoders for anomaly detection in videos. Pattern Recogn Lett 105:13–22
Sabokrou M, Fathy M, Hoseini M (2016) Video anomaly detection and localisation based on the sparsity and reconstruction error of auto-encoder. Electron Lett 52(13):1122–1124
Sabokrou M, Fayyaz M, Fathy M, Klette R (2017) Deep-cascade: cascading 3D deep neural networks for fast anomaly detection and localization in crowded scenes. IEEE Trans Image Process 26(4):1992–2004
Sabokrou M, Fayyaz M, Fathy M, Moayed Z, Klette R (2018) Deepanomaly: fully convolutional neural network for fast anomaly detection in crowded scenes. Comput Vis Image Underst 172:88–97
Sodemann AA, Ross MP, Borghetti BJ:BJ (2012) A review of anomaly detection in automated surveillance. IEEE Trans Syst Man Cybern Part C Appl Rev 42(6):1257–1272
Tran HT, Hogg DC. (2017) Anomaly detection using a convolutional winner
Vu, H., Phung, D., Nguyen, T.D., Trevors, A., Venkatesh, S. (2017) Energy-based Models for Video Anomaly Detection. arXiv preprint arXiv:1708.05211
Wang, J., Xu, Z. (2015) Crowd anomaly detection for automated video surveillance
Wang S, Zhu E, Yin J, Porikli F (2018) Video anomaly detection and localization by local motion based joint video representation and OCELM. Neurocomputing 277:161–175
Wang T, Qiao M, Deng Y, Zhou Y, Wang H, Lyu Q, Snoussi H (2018) Abnormal event detection based on analysis of movement information of video sequence. Optik-International Journal for Light and Electron Optics 152:50–60
Wang, X., Xie, W., Song, J. (2018) Learning Spatiotemporal Features With 3DCNN and ConvGRU for Video Anomaly Detection
Wang T, Qiao M, Lin Z, Li C, Snoussi H, Liu Z, Choi C (2019) Generative neural networks for anomaly detection in crowded scenes. IEEE Transactions on Information Forensics and Security 14(5):1390–1399
Xingjian SHI, Chen Z, Wang H, Yeung DY, Wong WK, Woo W (2015) Convolutional LSTM network: A machine learning approach for precipitation nowcasting
Xu D, Yan Y, Ricci E, Sebe N (2017) Detecting anomalous events in videos by learning deep representations of appearance and motion. Comput Vis Image Underst 156:117–127
Yi Y, Li X, Zhao R, Bi C, Wang J, Sun H. (2016) A constrained sparse representation approach for video anomaly detection
Yuan Y, Feng Y, Lu X (2018) Structured dictionary learning for abnormal event detection in crowded scenes. Pattern Recogn 73:99–110
Zhang Y, Lu H, Zhang L, Ruan X (2016) Combining motion and appearance cues for anomaly detection. Pattern Recogn 51:443–452
Zhang Y, Lu H, Zhang L, Ruan X, Sakai S (2016) Video anomaly detection based on locality sensitive hashing filters. Pattern Recogn 59:302–311
Zhao Y., Deng B, Shen C, Liu Y, Lu H, Hua XS (2017) Spatiotemporal autoencoder for video anomaly detection. pp. 1933–1941
Zhou S, Shen W, Zeng D, Fang M, Wei Y, Zhang Z (2016) Spatialtemporal convolutional neural networks for anomaly detection and localization in crowded scenes, vol. 47
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Ramchandran, A., Sangaiah, A.K. Unsupervised deep learning system for local anomaly event detection in crowded scenes. Multimed Tools Appl 79, 35275–35295 (2020). https://doi.org/10.1007/s11042-019-7702-5
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DOI: https://doi.org/10.1007/s11042-019-7702-5