An Efficient Location And Vehicle Tracking Scheme For Event Detection Using Its

Abstract

The contribution of Intelligent Transportation Systems (ITS) has gained a significant importance in real-world by granting road-side communication and safety services. Road side event detection and vehicle tracking are facilitated with the information gained from the vehicles. Other than reliable transmission, efficient processing and analysis of the vehicle related information helps to improve the rate of accuracy in detecting events. This article introduces selective vehicle tracking and localization scheme (STLS) for processing transmitted information efficiently. This scheme is focused to reduce the errors in processing to classify events in road segments, irrespective of the density and physical attributes of the vehicles. The scheme exploits constraint based fuzzy normalization for preparing decisions and arriving at optimal solutions. The classification of events based on the physical attributes and constraints helps to reduce processing errors, improving the accuracy of event detection and vehicle tracking.