Adaptive algorithms for automated intruder detection in surveillance networks
Publisher© 2014 Institute of Electrical and Electronics Engineers Inc.
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CitationAhmed, T., Pathan, A. -. K., & Ahmed, S. (2014). Adaptive algorithms for automated intruder detection in surveillance networks. Paper presented at the Proceedings of the 2014 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2014, 2775-2780. doi:10.1109/ICACCI.2014.6968617
Many types of automated visual surveillance systems have been presented in the recent literature. Most of the schemes require custom equipment, or involve significant complexity and storage needs. After studying the area in detail, this work presents four novel algorithms to perform automated, real-time intruder detection in surveillance networks. Built using machine learning techniques, the proposed algorithms are adaptive and portable, do not require any expensive or sophisticated component, are lightweight, and efficient with runtimes of the order of hundredths of a second. Two of the proposed algorithms have been developed by us. With application to two complementary data sets and quantitative performance comparisons with two representative existing schemes, we show that it is possible to easily obtain high detection accuracy with low false positives.