Abstract
Due to increase of attacks in public places, security has now become a major issue in public places. In this paper, we have proposed an abandoned object detection through video surveillance with real-time alarming. We use dual background subtraction method to find out the static object. It is been assumed that object which is part of foreground for longer period of time slowly turns as part of background. Background modelling is done using approximate median model. For foreground processing background subtracting method is followed by ANDing operation of frames to find out the static object. The system is simple to design and not having complexity of filters or complex calculation. PETS 2006 database is used for testing algorithm. The result shows satisfactory performance even under complex condition of shadow, moving crowd, and lightning condition. MATLAB R2013a is used for compilation of system.
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References
Mathew, R., Yu, Z., Zhang, J.: Detecting new stable objects in surveillance video. In: Proceedings of the IEEE 7th Workshop on Multimedia Signal Processing, pp. 1–4 (2005)
Bird, N., Atev, S., Caramelli, N., Martin, R., Masoud, O., Papanikolopoulos, N.: Real time, online detection of abandoned objects in public areas. In: Proceedings of IEEE International Conference on Robotics and Automation, pp. 3775–3780 (2006)
Singh, A., Hanmandlu, M., Madasu, V.K., Lovell, B.C.: An abandoned object detection system based on dual background segmentation. In: Proceedings of Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance, pp. 352–357 (2009)
Auvinet, E., Grossmann, E., Rougier, C., Dahmane, M., Meunier, J.: Left-luggage detection using homographies and simple heuristics. In: Proceedings of IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (PETS), New York, pp. 51–58 (2006)
Friedman, N., Russell, S.: Image segmentation in video sequences: a probabilistic approach. In: Proceedings of 13th Annual Conference on Uncertainty in Artificial Intelligence, pp. 175–181 (1997)
Stauffer, C., Grimson, W.: Adaptive background models for realtime tracking. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 246–252 (1999)
Wren, C., Azarbayejani, A., Darrell, T., Pentland, A.: Pfinder: real time tracking of the human body. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 780–785 (1997)
McFarlane, N., Schofield, C.: Segmentation and tracking of pigletsin images. Mach. Vis. Appl. 8(3), 187–193 (1995)
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Aditya Gupta, Stapute, V.R., Kulat, K.D., Neeraj Bokde (2016). Real-Time Abandoned Object Detection Using Video Surveillance. In: Afzalpulkar, N., Srivastava, V., Singh, G., Bhatnagar, D. (eds) Proceedings of the International Conference on Recent Cognizance in Wireless Communication & Image Processing. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2638-3_94
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DOI: https://doi.org/10.1007/978-81-322-2638-3_94
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