Abstract—This paper presents a robust vehicle detection approach using Haar-like feature. It is possible to get a strong edge feature from this Haar-like feature. Therefore it is very effective to remove the shadow of a vehicle on the road. And we can detect the boundary of vehicles accurately. In the paper, the vehicle detection algorithm can be divided into two main steps. One is hypothesis generation, and the other is hypothesis verification. In the first step, it determines vehicle candidates using features such as a shadow, intensity, and vertical edge. And in the second step, it determines whether the candidate is a vehicle or not by using the symmetry of vehicle edge features. In this research, we can get the detection rate over 15 frames per second on our embedded system. Keywords—vehicle detection, haar-like feauture, single camera, real time And hypothesis verification step is separated into two methods: the template and appearance based method . They are also based on the training or learning algorithm since the execution time of methods using databases tends too slow to be applied to an embedded system. In order to use it on the real-time system, we use edge features of vehicles without using databases. To solve the problem, we propose a vehicle detection algorithm using Haar-like edge features . By using this Haar-like feature we can get more accurate and faster result. This paper’s overall flowchart is as follows. I
To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.