This paper is dedicated to detecting and counting vehicles in day environment by using real time traffic flux through differential techniques. The basic idea used is variation in the traffic flux density due to presence of vehicle in the scene. In the present work a simple differential algorithm is designed and tested with vehicle detection and counting application. Traffic flux estimation will play vital role in implementing vehicle detection and counting scheme. Real time dynamic scene analysis has become very important aspect as the increase in video analysis. The technique developed is having simple statistical background. Dynamic selection of images from the sequence is implemented successfully in order to reduce the computation time. The designed technique are evaluated such a 20 different video sequences and weighed thoroughly with simple confidence measures. In the present work we have achieved real time analysis with normal video rate of 15 and 30 frames per second. And for vehicle count computation we are taking specific frame period (such as 2,5,10 etc), normal subtraction for vehicle count done on the basis of frame period. The result produced with this analysis is extremely good and beneficial in real time traffic control, detecting and counting vehicles in urban areas. MATLAB image processing tool box is explored to implement the technique. In the normal condition the average accuracy raised near to 95%.

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Traffic congestion and violation of traffic rules are very common in most of the road transport system. Continuous monitoring is becoming difficult. To improve the quality of road transport monitoring and control, the best possible alternative is machine vision. In this review, several works by researchers on traffic analysis are detailed, studied and reviewed critically for the purpose. Further, an attempt is made to classify the different road traffic analysis approaches available in the literature. Classification is based on principle used, algorithm adopted, techniques used, technology behind and other special considerations of the researchers.

Traffic congestion and violation of traffic rules are very common in most of the road transport system. Continuous monitoring is becoming difficult. To improve the quality of road transport monitoring and control, the best possible alternative is machine vision. In this review, several works by researchers on traffic analysis are detailed, studied and reviewed critically for the purpose. Further, an attempt is made to classify the different road traffic analysis approaches available in the literature. Classification is based on principle used, algorithm adopted, techniques used, technology behind and other special considerations of the researchers.