Localized Background Subtraction FeatureBased Approach for Vehicle Counting This paper presents vehicle counting using local features derived from background subtraction method combined with the traditional SVM classier. The main advantage of the proposed method is that it manages to overcome counting problem in different challenging situations such as during rainy day condition, which is yet a difficult problem in the standard background subtraction method. In this method, the background subtraction is performed within a small predefined region of a lane or road, thus increasing the computational speed. Next, two key features i.e. area covered by the motion indexed graph and number of edge pixel computed in the designated areas in the image are computed to form a 2D feature vector. Finally, we use SVM for classifying this vector into either vehicle or noise. Comparisons between the proposed method and other method show the potential of our approach.

Measuring Of RealTime Traffic Flow Using Video From Multiple IPBased Cameras Developing an intelligent transportation system that contains the basic information of the realtime traffic flow helps Lembaga Lebuhraya Malaysia to monitor and manage the traffic flow of our country. The main purpose of this paper is to detect and count vehicles along with its direction either towards North or South in realtime traffic flow by using multiple IPbased cameras. Open Source Computer Vision Library (OpenCV) and Python Programming language is used to implement the method developed. The project also provides an improvement by developing the system to be implemented in multiple cameras for realtime traffic measurement. The number of vehicles and their directions which are the output of the developed system is stored in json file to keep track of the data as well as the name of cameras with the time it is processed. The system developed is hoped to be a starting point in developing a more complex system.