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The Frontiers of Society, Science and Technology, 2020, 2(6); doi: 10.25236/FSST.2020.020619.

Conception of Campus Security Surveillance and Alarm System Based on Moving Object Detection


Wang Qian

Corresponding Author:
Wang Qian

Xi'an Institute of Aeronautics, Xi'an 710077, China


In the massive amount of surveillance video data, users only need to extract a small amount of useful information of key events, and these events are only small-probability events. How to obtain the information you want from a huge amount of data, or how to achieve intelligent monitoring of the events that have occurred, has become a new research direction for security surveillance systems. This article explores an optimized object search calculation method for a hybrid Gaussian model. This calculation method uses the background difference method to find moving objects, and uses an optimized hybrid Gaussian model to reconstruct the background picture, which significantly improves the recognition accuracy of moving target detection. In the past, monitoring only stored audio and video without alarming itself. The situation has changed, which makes the security monitoring system able to provide real-time alarm messages for the school's security staff on duty, and it is hoped that the campus's emergency response capabilities can be significantly improved.


Video surveillance, Hybrid gaussian model, Moving target detection

Cite This Paper

Wang Qian. Conception of Campus Security Surveillance and Alarm System Based on Moving Object Detection. The Frontiers of Society, Science and Technology (2020) Vol. 2 Issue 6: 88-92. https://doi.org/10.25236/FSST.2020.020619.


[1] Kalimov, A. G., Svedentsov, M. L (1996). Three-dimensional magnetostatic field calculation using integro-differential equation for scalar potential. IEEE Transactions on Magnetics, vol.32, no.3, pp.667-670.
[2] Bruce D. Lucas, & Takeo Kanade. (1985). Optical Navigation by the Method of Differences. Proceedings of the 9th international joint conference on Artificial intelligence, no.2, pp.12-13..
[3] B McCane, K Novins, D Crannitch, B Galvin (2001). On benchmarking optical flow. Computer Vision & Image Understanding, vol.84, no.1, pp.126-143.
[4] Letian Kang, Lanjun Wan, Kenli Li (2014). Efficient Parallelization of a Two-List Algorithm for the Subset-Sum Problem on a Hybrid CPU/GPU Cluster. 2014 Sixth International Symposium on Parallel Architectures, Algorithms and Programming (PAAP). IEEE.
[5] Tao, M., Bai, J., Kohli, P., et al (2012). Simpleflow: a non-iterative, sublinear optical flow algorithm. Computer Graphics Forum, no.31, pp.345-353.
[6] Islam, M. T , Chaudhry, A , Tang, S, et al (2018). A new method for estimating the effective poisson’s ratio in ultrasound poroelastography. IEEE Transactions on Medical Imaging, pp.1-2.