Vehicle Detection and Classification Using Wireless Sensor Network

There is growing need to enhance the performance of contemporary vehicle surveillance technologies using the advances in Wireless Sensor Networks (WSN)Technology. It is widely proposed to use recent advances in WSN technologies for vehicle detection and classification. Traffic surveillance systems provide data for building an efficient Intelligent Transportation System (ITS). Flexibility, ease in installation and maintenance, low cost and high accuracy of WSN gives more accurate vehicle detection and classification. This paper proposes a simple, yet, powerful system for real time vehicle classification. The approach is an improvisation and advancement of already developed algorithms. Presented algorithm is used for extraction of feature vectors. The experiment detects vehicles using detection state machine (DSM) and classifies them through K-Nearest Neighbouralgorithm. This classifier classifies the vehicles, into their stipulated classes, using its magnetic signature's shape with nearly 100% accuracy. It has been verified that the algorithm enhances the performance of vehicle surveillance with high precision and robustness.