Poster abstract: A machine learning approach for vehicle classification using passive infrared and ultrasonic sensors

Abstract
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.

Citation
Warriach, E. U., & Claudel, C. (2013). Poster abstract. Proceedings of the 12th International Conference on Information Processing in Sensor Networks - IPSN ’13. doi:10.1145/2461381.2461434

Publisher
Association for Computing Machinery (ACM)

Journal
Proceedings of the 12th international conference on Information processing in sensor networks - IPSN '13

Conference/Event Name
12th International Conference on Information Processing in Sensor Networks, IPSN 2013 - Part of CPSWeek 2013

DOI
10.1145/2461381.2461434

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