Vehicle and Pedestrian Detection in eSafety Applications

This paper describes a target detection system on road environments based on Support Vector Machine (SVM) and monocular vision. The final goal is to provide pedestrian-to-car and car-to-car time gap. The challenge is to use a single camera as input, in order to achieve a low cost final system that meets the requirements needed to undertake serial production in automotive industry. The basic feature of the detected objects are first located in the image using vision and then combined with a SVMbased classifier. An intelligent learning approach is proposed in order to better deal with objects variability, illumination conditions, partial occlusions and rotations. A large database containing thousands of object examples has been created for learning purposes. The classifier is trained using SVM in order to be able to classify pedestrians, cars and trucks. In the paper, we present and discuss the results achieved up to date in real traffic conditions.