Ehsan Ullah Warriach
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.
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Digital Library
- C. yee Chong, Ieee, S. P. Kumar, and S. Member. Sensor networks: evolution, opportunities, and challenges. Proc. of the IEEE, 2003.Google Scholar
- H. Zhang, A. Berg, M. Maire, and J. Malik. Svm-knn: Discriminative nearest neighbor classification for visual category recognition. In IEEE CVPR, 2006. Google ScholarDigital Library
- H. Zhang and J. Su. Naive bayesian classifiers for ranking. In 15th ECML. Springer, 2004.Google Scholar
Index Terms
- Poster abstract: a machine learning approach for vehicle classification using passive infrared and ultrasonic sensors
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