A Distributed Threshold Algorithm for Vehicle Classification Based on Binary Proximity Sensors and Intelligent Neuron Classifier

• Published in: Journal of Information Science and Engineering Vol. 26; no. 3; pp. 769 - 783
• Main Authors: 張偉(Wei Zhang), 譚國真(Guo-Zhen Tan), 史慧敏(Hui-Min Shi), 林明文(Ming-Wen Lin)
• Format: Journal Article
• Language: English
• Published: 社團法人中華民國計算語言學學會 01.05.2010
• Subjects: Adaptive algorithms; Algorithms; Classification; Classifiers; Computer simulation; Distortion; Low cost; Magnetic fields; Magnetic signatures; Methodology; Networks; Neural networks; Neurons; Proximity; Real time; Recognition; Robustness; Sensors; Surveillance; Thresholds; Vehicles; vehicle detection; adaptive; wireless sensor networks; real-time traffic surveillance; intelligent neurons; distributed threshold; clustering; vehicle classification; binary proximity sensor networks
• ISSN: 1016-2364
• DOI: 10.6688/JISE.2010.26.3.3

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.