Abstract
Due to increasing demand for security, the instant detection of abnormal behavior in video surveillance systems becomes a critical issue in a smart surveillance system. The currently applied semiautomatic systems mainly depend on human intervention to detect the abnormal activities and suspicious human behaviours from video context. Due to these limitations, it has become an urgent need for intelligence systems to avoid the very slow response and reduce the human observer and interventions. In this paper, a method that can trace abnormalities of human behaviour from video is presented. Techniques related to bounding box measurements and descriptions for behaviour representation were used. Moreover, the performance evaluation of the proposed method is presented.
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
Borkar A, Nagmode MS, Pimplaskar D (2013) Real time abandoned bag detection using OpenCV. Int J Sci Eng Res 4(11):660
Bouwmans T (2014) Traditional and recent approaches in background modeling for foreground detection: an overview. Comput Sci Rev 11:31–66
Butler DE, Bove VM, Sridharan S (2005) Real-time adaptive foreground/background segmentation. EURASIP J Adv Signal Process (14):1–3
Culibrk D, Marques O, Socek D, Kalva H, Furht B (2007) Neural network approach to background modeling for video object segmentation. IEEE Trans Neural Networks 18(6):1614–1627
Mishra MS, Jtmcoe F, Bhagat KS (2015) A survey on human motion detection and surveillance. Int J Adv Res Electron Commun Eng (IJARECE) 4
Wang WJ, Chang JW, Haung SF, Wang RJ (2016) Human posture recognition based on images captured by the Kinect sensor. Int J Adv Robot Syst 13(2):54
Htike KK, Khalifa OO, Mohd Ramli HA, Abushariah MAM (2014) Human activity recognition for video surveillance using sequences of postures. In: 3rd International Conference on e-Technologies and Networks for Development, pp 79–82
Lavee G, Khan L, Thuraisingham B (2007) A framework for a video analysis tool for suspicious event detection. Multimedia Tools Appl 35(1):109–123
Hu Y (2020) Design and implementation of abnormal behavior detection based on deep intelligent analysis algorithms in massive video surveillance. J Grid Computing 18(2):227–237
Zhong H, Shi I, Visontai M (2004) Detecting unusual activity in video. In: 2004 IEEE Computer Vision and Pattern Recognition, vol 2, pp II-819–II-826
Khan ZA, Sohn W (2011) Abnormal human activity recognition system based on R-transform and kernel discriminant technique for elderly home care. IEEE Trans Consumer Electron 57(4):1843–1850
Acknowledgements
This paper was supported in part by Fundamental Research Grant Scheme, Ministry of Higher Education, Malaysia (FRGS19-017-0625).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Khalifa, O.O., Abdul Khodir, H., Abdul Malik, N., Abdul Malek, N.F. (2022). Video-Based Abnormal Behaviour Detection in Smart Surveillance Systems. In: Isa, K., et al. Proceedings of the 12th National Technical Seminar on Unmanned System Technology 2020. Lecture Notes in Electrical Engineering, vol 770. Springer, Singapore. https://doi.org/10.1007/978-981-16-2406-3_26
Download citation
DOI: https://doi.org/10.1007/978-981-16-2406-3_26
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-2405-6
Online ISBN: 978-981-16-2406-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)