This article describes the implementation of four different machine learning techniques for vehicle classification in a dual ultrasonic/passive infrared traffic flow sensors. Using k-NN, Naive Bayes, SVM and KNN-SVM algorithms, we show that KNN-SVM significantly outperforms other algorithms in terms of classification accuracy. We also show that some of these algorithms could run in real time on the prototype system. Copyright © 2013 ACM.
|Title of host publication
|Proceedings of the 12th international conference on Information processing in sensor networks - IPSN '13
|Association for Computing Machinery (ACM)
|Number of pages
|Published - 2013