Abstract
An adaptive framework for building intelligent measurement systems has been proposed in the paper and tested on simulated traffic surveillance data. The use of the framework enables making intelligent decisions related to the presence of anomalies in the surveillance data with the help of statistical analysis, computational intelligent and machine learning. Computational intelligence can also be effectively utilised for identifying the main contributing features in detecting anomalous data points within the surveillance data. The experimental results have demonstrated that a reasonable performance is achieved in terms of inferential accuracy and data processing speed.
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Acknowledgment
The authors would like to acknowledge the contribution of their industrial partner – Selex ES, a subsidiary of Finmeccanica Company – for providing the traffic simulation model, funding and general support for the work on this research project.
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Petrovski, A., Rattadilok, P., Petrovskii, S. (2016). Intelligent Measurement in Unmanned Aerial Cyber Physical Systems for Traffic Surveillance. In: Jayne, C., Iliadis, L. (eds) Engineering Applications of Neural Networks. EANN 2016. Communications in Computer and Information Science, vol 629. Springer, Cham. https://doi.org/10.1007/978-3-319-44188-7_12
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DOI: https://doi.org/10.1007/978-3-319-44188-7_12
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