TY - GEN
T1 - Poster abstract: A machine learning approach for vehicle classification using passive infrared and ultrasonic sensors
AU - Warriach, Ehsan Ullah
AU - Claudel, Christian G.
N1 - KAUST Repository Item: Exported on 2020-10-01
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
UR - http://hdl.handle.net/10754/564657
UR - http://dl.acm.org/citation.cfm?doid=2461381.2461434
UR - http://www.scopus.com/inward/record.url?scp=84876780900&partnerID=8YFLogxK
U2 - 10.1145/2461381.2461434
DO - 10.1145/2461381.2461434
M3 - Conference contribution
SN - 9781450319591
SP - 333
EP - 334
BT - Proceedings of the 12th international conference on Information processing in sensor networks - IPSN '13
PB - Association for Computing Machinery (ACM)
ER -