References

Agrawal, S. and J. Agrawal, 2015. Survey on anomaly detection using data mining techniques. Proceedings of the 19th International Conference on Knowledge Based and Intelligent Information and Engineering Systems, September 7-9, 2015, Elsevier, Amsterdam, Netherlands, pp: 708-713.

Andersson, M., J. Rydell, S.L. Laurent, D. Prevost and F. Gustafsson, 2012. Crowd analysis with target tracking, K-means clustering and hidden Markov models. Proceedings of the 2012 15th International Conference on Information Fusion (FUSION), July 9-12, 2012, IEEE, Singapore, ISBN:978-1-4673-0417-7, pp: 1903-1910.

Borkar, A., M.S. Nagmode and D. Pimplaskar, 2013. Real time abandoned bag detection using OpenCV. Intl. J. Sci. Eng. Res., 4: 660-666.

Chandola, V., A. Banerjee and V. Kumar, 2009. Anomaly detection: A survey. ACM. Comput. Surv. CSUR., 41: 1-15.
CrossRef  |  Direct Link  |  

Chuang, C.H., J.W. Hsieh, L.W. Tsai, S.Y. Chen and K.C. Fan, 2009. Carried object detection using ratio histogram and its application to suspicious event analysis. IEEE. Trans. Circuits Syst. Video Technol., 19: 911-916.
CrossRef  |  Direct Link  |  

Deepika, R., A.V. Prasath, M. Indhumathi, A.P. Kumar and V. Vaidehi, 2017. Anomalous event detection in traffic video surveillance based on temporal pattern analysis. J. Comput., 12: 190-199.
Direct Link  |  

Dhanachandra, N., K. Manglem and Y.J. Chanu, 2015. Image segmentation using K-means clustering algorithm and subtractive clustering algorithm. Procedia Comput. Sci., 54: 764-771.
Direct Link  |  

Gunale, K.G. and P. Mukherji, 2015. Fall detection using k-nearest neighbor classification for patient monitoring. Proceedings of the International Conference on Information Processing (ICIP), December 16-19, 2015, IEEE, Pune, India, ISBN:978-1-4673-7758-4, pp: 520-524.

Harrou, F., N. Zerrouki, Y. Sun and A. Houacine, 2016. A simple strategy for fall events detection. Proceedings of the 2016 IEEE 14th International Conference on Industrial Informatics (INDIN), July 19-21, 2016, IEEE, Poitiers, France, ISBN:978-1-5090-2871-9, pp: 332-336.

Jiang, H., Y. Lu and J. Xue, 2016. Automatic soccer video event detection based on a Deep Neural Network Combined CNN and RNN. Proceedings of the 2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI), November 6-8, 2016, IEEE, San Jose, California, USA., ISBN:978-1-5090-4459-7, pp: 490-494.

Kwon, E., S. Noh, M. Jeon and D. Shim, 2013. Scene modeling-based anomaly detection for intelligent transport system. Proceedings of the 2013 4th International Conference on Intelligent Systems Modelling and Simulation (ISMS), January 29-31, 2013, IEEE, Bangkok, Thailand, ISBN:978-1-4673-5653-4, pp: 252-257.

Lane, B., M. Poole, M. Camp and K.J. Murray, 2016. Using machine learning for advanced anomaly detection and classification. Proceedings of the 17th AMOS International Conference on Advanced Maui Optical and Space Surveillance Technologies, September 20-23, 2016, AMOS, Maui, Hawaii, USA., ISBN:978-1-5108-3288-6, pp: 1-6.

Leeuwen, V.C., A. Halma and K. Schutte, 2013. Anomalous human behavior detection: An adaptive approach. Proceedings of the SPIE Conference on Defense, Security and Sensing, May 23, 2013, SPIE, Bellingham, Washington, USA., pp: 874519-874519.

Li, Q. and W. Li, 2016. A novel framework for anomaly detection in video surveillance using multi-feature extraction. Proceedings of the 2016 9th International Symposium on Computational Intelligence and Design (ISCID) Vol. 1, December 10-11, 2016, IEEE, Hangzhou, China, ISBN:978-1-5090-3558-8, pp: 455-459.

Rao, K.H., G. Srinivas, A. Damodhar and M.V. Krishna, 2011. Implementation of anomaly detection technique using machine learning algorithms. Intl. J. Comput. Sci. Telecommun., 2: 25-31.
Direct Link  |  

Sabokrou, M., M. Fayyaz, M. Fathy and R. Klette, 2017. Deep-cascade: Cascading 3d deep neural networks for fast anomaly detection and localization in crowded scenes. IEEE. Trans. Image Process., 26: 1992-2004.
CrossRef  |  Direct Link  |  

Sandhan, T., T. Srivastava, A. Sethi and J.Y. Choi, 2013. Unsupervised learning approach for abnormal event detection in surveillance video by revealing infrequent patterns. Proceedings of the 2013 28th International Conference on Image and Vision Computing New Zealand (IVCNZ), November 27-29, 2013, IEEE, Wellington, New Zealand, ISBN:978-1-4799-0883-7, pp: 494-499.

Wang, G., H. Fu and Y. Liu, 2016. Real time abnormal crowd behavior detection based on adjacent flow location estimation. Proceedings of the 2016 4th International Conference on Cloud Computing and Intelligence Systems (CCIS), August 17-19, 2016, IEEE, Beijing, China, ISBN:978-1-5090-1256-5, pp: 476-479.

Xiao, T., C. Zhang and H. Zha, 2015. Learning to detect anomalies in surveillance video. IEEE. Signal Process. Lett., 22: 1477-1481.
CrossRef  |  Direct Link  |  

Yu, E. and P. Parekh, 2016. A Bayesian ensemble for unsupervised anomaly detection. Cornell University, Ithaca, New York. https://arxiv.org/abs/1610.07677