English

Abstract

Detection of vehicle in traffic provides the details about vehicle and gives better understanding about traffic. Traffic planners can obtain detailed information on the numbers and types of vehicles using a section of road. This allows them to adjust maintenance schedules and lets enforcement authorities know where and when to monitor for overweight vehicles without carrying out expensive manual operations on every road. Detection of vehicles in images represents an important step towards achieving automated roadway monitoring capabilities. The challenge lies in being able to reliably and quickly detect multiple small objects of interest against a cluttered background which usually consists of road signs, tress and buildings. To this end present a proof of concept Traffic monitoring application. The application counts the number of cars passing in either direction. Car detection is done using a boosted cascade of Haar features and is combined with the pyramidal KLT tracker to achieve a fast monitoring system.