Vehicle detection and tracking based on corner and lines adjacent detection features

This paper discusses a new method in detecting moving objects, which is differ from most of the methods used such as Gaussian Mixture Model, and Haar-Like approach. The focus is on utilizing corner detection and line adjacent detection features through thresholding process creating black and white images to detect the corner of each object in each frame. The process divides a frame length into 4 parts, whereas the first part acted as initiation process of moving object recognition while the rest of the frame functioning as vehicle tracking, speed measurement, and number of vehicles calculation. The initiation process started by identifying corner spots of the moving objects that must be recognized as a single object. The lines surpassing through two points are later identified to determine whether those spots have dark color (0) or light color (1). The moving objects is represented by light color (1) and the walking objects is represented by dark color (0). A group of corner spots, identified and connected by two-point-line equation to be recognized as one unified object by using corner and line adjacent method. The identified vehicle objects can be more easily tracked and identified by the average speed in order to obtain the number of passing vehicles. The research result shows that in the initiation phase, the corner and line adjacent features able to detect moving object and distinguish it with different objects. Furthermore, in tracking phase, system is able to track the vehicle position, measuring the speed and number of vehicles. The system is proven to be able to recognize the moving objects quickly and accurately resulting in the more feasible process of speed measurement and tracking.