Title :
Inferential measurements for situation awareness
Author :
Rattadilok, Prapa ; Petrovski, Andrei
Author_Institution :
Sch. of Comput. Sci. & Digital Media, Robert Gordon Univ., Aberdeen, UK
Abstract :
The paper proposes a generic approach to building inferential measurement systems. The large amount of data needed to be acquired and processed by such systems necessitates the use of machine learning techniques. In this study, an inferential measurement system aimed at enhancing situation awareness has been developed and tested on simulated traffic surveillance data. The performance of several Computational Intelligence techniques within this system has been examined and compared on the data containing anomalous driving patterns.
Keywords :
learning (artificial intelligence); traffic engineering computing; anomalous driving patterns; computational intelligence techniques; inferential measurement systems; machine learning; simulated traffic surveillance data; situation awareness; traffic surveillance; Artificial neural networks; Buildings; Computational intelligence; Computational modeling; Surveillance; Traffic control; Vehicles; anomaly detection; inferential measurement; machine learning; situation awareness; unmanned aerial vehicles;
Conference_Titel :
Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA), 2013 IEEE International Conference on
Conference_Location :
Milan
Print_ISBN :
978-1-4673-4701-3
DOI :
10.1109/CIVEMSA.2013.6617402
