Traffic density estimation, vehicle classification and stopped vehicle detection for traffic surveillance system using predefined traffic videos

Introduction The traffic video monitoring and surveillance systems have been widely used in traffic management. Most of the companies have started to use several cameras for the use of traffic surveillance system. The surveillance system extracting useful information such as traffic density, vehicle types from these camera systems has become a hassle due to the high number of cameras in use. Manual analysis of these camera systems is now unapplicable. Development of intellegent systems that extract traffic density and vehicle classification information from traffic surveillance systems is crucial in traffic management. It is important to know the traffic density of the roads real time especially in mega cities for signal control and effective traffic management. Time estimation of reaching from one location to another and recommendation of different route alternatives using real time traffic density information are very valuable for mega city residents. In addition, vehicle classification (big: truck, middle: van, or small: car) is also important for traffic control centers. For example, the effects of banning big vehicles from a road can be analyzed using vehicle classification information in a simulation program. This paper presents an automatic traffic density estimation and vehicle classification method for traffic surveillence system using neural networks. Several other vehicle detectors such as loop, radar, infrared, ultrasonic, and microwave detectors exist in the literature. These sensors are expensive with limited capacity and involve installation, maintenance, and implementation difficulties. For example, loop sensor might need maintenance due to road ground deformation or metal barrier near the road might prevent effective detection using radar sensors [1]. In resent years, video processing techniques have attracted researchers for vehicle detection [2-7]. Detection of moving objects including vehicle, human, etc. in video can be achieved in three main approaches: Temporal difference, optical flow, and background substraction. In temporal difference, the image difference of two consecutive image frames are obtained [12-18]. However, this approach has some limitations such as visual homogeneity requirement and its effectiveness depends on the speeds of moving objects [2]. Optical flow method was developed to obtain effective background modification, which bases on the detection of intensity changes [2]. However, illumunation change due to weather or sun-light reflections decreases its effectiveness. It is also computationally inefficient [2]. The third method, background subtraction, is the mostly seen method in the literature for effective motion tracking and moving object identification [2, 4, 6, 9, 10, 11]. In background subtraction, background can be static, in which a fixed background is obtained beforehand and used in the entire process; or dynamic, in which background is dynamically updated with changing external effects like weather. Static background may not be effective in most applications, many methods include dynamic background subtraction. In [19], the background is detected dynamically by using dynamic threshold selection method. In [22], land mark based method and BS&Edge method are used to remove the shadow from the scene. Different classification techniques have been employed after the moving objects are detected in order to identify the