Intelligent measurement in unmanned aerial cyber physical systems for traffic surveillance.
Petrovski, Andrei; Rattadilok, Prapa; Petrovskii, Sergey
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.
|Start Date||Sep 2, 2016|
|Publication Date||Sep 30, 2016|
|Publisher||Springer (part of Springer Nature)|
|Series Title||Communications in computer and information science|
|Institution Citation||PETROVSKI, A., RATTADILOK, P. and PETROVSKII, S. 2016. Intelligent measurement in unmanned aerial cyber physical systems for traffic surveillance. In Jayne, C. and Iliadis, L. (eds.) Engineering applications of neural networks: proceedings of the 17th International engineering applications of neural networks conference (EANN 2016), 2-5 September 2016, Aberdeen, UK. Communications in computer and information science, 629. Cham: Springer [online], pages 161-175. Available from: https://doi.org/10.1007/978-3-319-44188-7_12 161-175. Available from: https://doi.org/10.1007/978-3-319-44188-7_12|
|Keywords||Intelligent measurement; Traffic surveillance; Data anomalies; Computational intelligence; Artificial neural networks; Cyber physical system|
PETROVSKI 2016 Intelligent measurement in unmanned