Embedded realtime feature fusion based on ANN, SVM and NBC

Artificial neural networks (ANNs), support vector machines (SVMs) and naive Bayes classifiers (NBCs) are common tools for multisensor data fusion applications. In this paper ANN, SVM and NBC are applied to embedded realtime feature fusion and compared to different algorithms concerning classification execution time as well as classification rate. These algorithms are implemented on our three-layered multisensor data fusion architecture and applied to traffic monitoring where we are focusing on fusing data originating from distributed acoustic, image and laser sensors for vehicle classification and tracking. The evaluation of the algorithms is performed on our embedded platform and has shown promising results concerning realtime classification execution time and classification rate.