Poster Abstract: A Machine Learning Approach for Vehicle Classification using Passive Infrared and Ultrasonic Sensors

被引：0

作者：

Warriach, Ehsan Ullah ^{[1
]}

Claudel, Christian ^{[2
]}

机构：

[1] Univ Groningen, Dept Math & Comp Sci, Groningen, Netherlands

[2] King Abdullah Univ Sci & Technol, Dept Elect Engn, Thuwal, Saudi Arabia

来源：

2013 ACM/IEEE INTERNATIONAL CONFERENCE ON INFORMATION PROCESSING IN SENSOR NETWORKS (IPSN) | 2013年

关键词：

Vehicle Classification; K-NN; SVM; Naive Bayes; Clustering;

D O I：

暂无

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：333 / 334

页数：2