Abstract
One of the primary tools for monitoring human movement and to prevent unwanted or unintended activities is surveillance camera. Nowadays, security management professional rely heavily on video surveillance to challenge crime and avert negative incidents that impact human society. Large numbers of surveillance camera installations have been increasing dramatically in the private and public sectors to monitor public activities. Video surveillance is one of the most effective methods to guarantee security. Fitting a surveillance camera simply transfers the captured video to security personnel. But the abnormal activities can be identified only by integrating an intelligent system to analyze the videos. Hence, this paper is motivated to design and implement an Intelligent Video Analytics Model (IVAM) also known as Human Object Detection (HOD) method for analyzing and detecting video-based abundant objects and abnormal human activities. IVAM can be deployed along with surveillance cameras in any public places like airport, hospital, shopping malls and railway station to automatically identify any abnormal event. IVAM is experimented with MATLAB software and the results are verified. The performance of IVAM is evaluated by comparing the obtained results with the existing approaches and it is proved that IVAM performs better compared to other contemporary methods in terms of accurately detecting the anomalies with less error rate and high classification accuracy.
Similar content being viewed by others
References
Chaturvedi, P.P., Rajput, A.S., Jain, A.: Video object tracking based on automatic background segmentation and updating using RBF neural network. Int. J. Adv. Comput. Res. 3, 866 (2013)
Kim, J., Grauman, K., Observe locally, infer globally: a space-time MRF for detecting abnormal activities with incremental updates. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, June (2009). pp. 2921–2928
Zhan B, Monekosso DN, Remagnino P, Velastin SA, Xi IQ, Crowd analysis: a survey. Mach. Vis. Appl. 19:345–357 (2008)
De Almeida IR, Jung, CR, Change detection in human crowds. In: Proceedings Conference on Graphics, Patterns and Images, August (2013), pp. 63–69
Oluwatoyin P, Popoola, Wang K, Video-based abnormal human behavior recognition—a review. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 42, 6, 2012, 865–878
Saykol, E., Gudukbay, U., Ulusoy, O.: Scenario-based query processing for video-surveillance archives. Eng. Appl. Artif. Intell. 23(3), 331–345 (2010)
Brezeale, D., Cook, D.J.: Automatic video classification: a survey of the literature, IEEE Transactions on System, Man and Cybernetics-Part C: Applications and Reviews, 38, 3, 416–430, 2008
Lavee, G., Rifling, E., Rudzsky, M.: Understanding video events: a survey of methods for automatic interpretation of semantic occurrences in video. Syst. Man Cybern. Part C Appl. Rev. 39, 5, 489–504, 2009
Buxton, H.: Learning and understanding dynamic scene activity: a review. Image Vis. Comput. 21, 1, 2003, 125–136
Hu, W., Tan, T., Wang, L., Maybank, S.: A survey on visual surveillance of object motion and behaviors. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 34, 3, 2004, 334–352
Haering, N., Venetianer, P.L., Lipton, A.: The evolution of video surveillance: an overview. Mach. Vis. Appl. 19, 5–6, 2008, 279–290
Raty, T.D.: Survey on contemporary remote surveillance systems for public safety. IEEE Trans. Syst., Man Cybern. Part C Appl. Rev. 40, 5, 2010, 493–515
Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. 41, 2009, 1–58
Patcha A, Park, J.: An overview of anomaly detection techniques: existing solutions and latest technological trends. Comput. Netw 51, 2007, 3448–3470
Vapnik, V.N., Lerner, A.: Pattern recognition using generalized portrait method. Autom. Remote Control 24, 774–780 (1963)
Cui, X., Liu, Q., Gao, M., Metaxas, D.N., Abnormal detection using interaction energy potentials. In: Proceedings of the 25th IEEE International Conference on Computer Vision and Pattern Recognition, Providence, RI, USA, June 16–21, 2012
Abdel-Basset, M., El-Shahat, D., Mirjalili, S.: A hybrid whale optimization algorithm based on local search strategy for the permutation flow shop scheduling problem. Future Gener Comput Syst 85, 129–145 (2018)
Abdel-Basset, M., Manogaran, G., Abdel-Fatah, L., Mirjalili, S.: An improved nature inspired meta-heuristic algorithm for 1-D bin packing problems. Pers. Ubiquitous Comput. 22(5–6), 1117–1132 (2018)
Abdel-Basset, M., Manogaran, G., Gamal, A., Smarandache, F.: A hybrid approach of neutrosophic sets and DEMATEL method for developing supplier selection criteria. Des. Autom. Embed. Syst. 22(3), 257–278 (2018)
Abdel-Basset, M., Gunasekaran, M., Mohamed, M., Smarandache, F.: A novel method for solving the fully neutrosophic linear programming problems. Neural Comput. Appl. (2018). https://doi.org/10.1007/s00521-018-3404-6
Abdel-Basset, M., Manogaran, G., Fakhry, A.E., El-Henawy, I.: 2-Levels of clustering strategy to detect and locate copy-move forgery in digital images. Multimed Tools Appl. (2018). https://doi.org/10.1007/s11042-018-6266-0
Abdel-Basset, M., Mohamed, M.: (2018). Internet of things (IoT) and its Impact on supply chain: a framework for building smart, secure and efficient systems. Future Gener Comput Syst
Abdel-Basset, M., Manogaran, G., Mohamed, M., Rushdy, E.: Internet of things in smart education environment: supportive framework in the decision-making process. Concurr. Comput. Pract. Exp. (2018). https://doi.org/10.1002/cpe.4515
Mehran, R., Oyama A, Shah M, Abnormal crowd behavior detection using social force model. In: Proceedings of the 22nd IEEE International Conference on Computer Vision and Pattern Recognition, Miami, FL, USA, June 20–26, 2009
BKP Horn, Schunck, B.G.: Determining optical flow. Artif Intell 17, 1981, 185–203
Weiming, Hu, Li, W., Zhang, X., Maybank, S.: Single and multiple object tracking using a multi-feature joint sparse representation. IEEE Trans Pattern Recognit Mach Intell 37(4), 816–833 (2015)
Lucas, B.D., Kanade, T., An iterative image registration technique with an application to stereo vision. In: Proceedings of the 7th International Joint Conference on Artificial Intelligence, Vancouver, British, Columbia, August 24–28, 1981
Shi, J., Tomasi, C., Good features to track. In: Proceedings of 7th IEEE International Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA, June 21–23, 1994
Daniilidis, K., Krauss, C., Hansen, M., Sommer, G.: Real-time tracking of moving objects with an active camera, Real-Time Imaging 4, 1998, 3–20
Huang, C., Chen, Y., Fu, L., Real-time object detection and tracking on a moving camera platform. In: Proceedings of IEEE ICCAS-SICE, Fukuoka, Japan, August 18–21, 2009
Wang T, Snoussi H, Detection of abnormal events via optical flow feature analysis. Sensors 15, 2015, 7156–7171
Schlkopf B, Smola AJ: Learning with Kernels: Support Vector Machines, Regularization, and Optimization and Beyond. MIT Press, Cambridge (2002)
Hoffmann, H.: Kernel PCA for novelty detection. Pattern Recognit 40 2007, 863–874
Cristianini, N., Shawe-Taylo, J.: An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. Cambridge University Press, Cambridge (2000)
Boiman, O., Irani, M.: Detecting irregularities in images and in video. Int. J. Comput. Vis. 74(1):17_31, 2007
Oikonomopoulos, A., Patras, I., Pantic, M.: Spatiotemporal localization and categorization of human actions in un-segmented image sequences. IEEE Trans. Image Process. 20(4):1126–1140, 2011
Bertini, M., Bimbo, A.Del, Seidenari, L.: Multi-scale and real-time nonparametric approach for anomaly detection and localization. Comput. Vis. Image Understanding 116(3):320–329 (2012)
Butt, A.A., Collins, R.T., Multi-target tracking by lagrangian relaxation to min-cost network flow. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013, pp. 1846–1853, 2013
Bertini, M., Bimbo Del A, Seidenari, L.: Multi-scale and realtime non-parametric approach for anomaly detection and localization. Compt. Vis. Image Und. 116(3), 320–329 (2012)
Reddy, V., Sanderson, C., Lovell, B.C.: Improved anomaly detection in crowded scenes via cell-based analysis of foreground speed, size and texture. In: CVPR Workshops, 55–61, 2011
Saligrama, V., Zhu, C.: Video anomaly detection based on local statistical aggregates. In: CVPR, pp. 2112–2119, 2012
Acknowledgements
The authors wish to acknowledge and thank the developers of UCSD-AD dataset which had been used in this experiment.
Funding
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Balasundaram, A., Chellappan, C. An intelligent video analytics model for abnormal event detection in online surveillance video. J Real-Time Image Proc 17, 915–930 (2020). https://doi.org/10.1007/s11554-018-0840-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11554-018-0840-6