Poster abstract: A machine learning approach for vehicle classification using passive infrared and ultrasonic sensors
KAUST DepartmentElectrical Engineering Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Distributed Sensing Systems Laboratory (DSS)
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AbstractThis 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.
JournalProceedings of the 12th international conference on Information processing in sensor networks - IPSN '13
Conference/Event name12th International Conference on Information Processing in Sensor Networks, IPSN 2013 - Part of CPSWeek 2013