Camera-based clear path detection
In using image analysis to assist a driver to avoid obstacles on the road, traditional approaches rely on various detectors designed to detect different types of objects. We propose a framework that is different from traditional approaches in that it focuses on finding a clear path ahead. We assume that the video camera is calibrated offline (with known intrinsic and extrinsic parameters) and vehicle information (vehicle speed and yaw angle) is known. We first generate perspective patches for feature extraction in the image. Then, after extracting and selecting features of each patch, we estimate an initial probability that the patch corresponds to clear path using a support vector machine (SVM) based probability estimator on the selected features. We finally perform probabilistic patch smoothing based on spatial and temporal constraints to improve the initial estimate, thereby enhancing detection performance. We show that the proposed framework performs well even in some challenging examples with shadows and illumination changes.