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论文类型：期刊论文

发表时间：2010-05-01

发表刊物：JOURNAL OF INFORMATION SCIENCE AND ENGINEERING

收录刊物：SCIE、EI、Scopus

卷号：26

期号：3,SI

页面范围：769-783

ISSN号：1016-2364

关键字：real-time traffic surveillance; vehicle detection; vehicle classification; wireless sensor networks; binary proximity sensor networks; intelligent neurons; distributed threshold; adaptive; clustering

摘要：To improve the accuracy of real time vehicle surveillance, utilize the advances in wireless sensor networks to develop a magnetic signature and length estimation based vehicle classification methodology with binary proximity magnetic sensor networks and intelligent neuron classifier. In this algorithm, we use the low cost and high sensitive magnetic sensors to measure the magnetic field distortion when vehicle crosses the sensors and detect vehicle via an adaptive threshold. The vehicle length is estimated with the geometrical characteristics of the proximity sensor networks, and finally identifies vehicle type from an intelligent neural network classifier. Simulation and on-road experiment obtains high recognition rate over 90%. It verified that this algorithm enhances the vehicle surveillance with high accuracy and solid robustness.