Water Hazard Detection for Intelligent Vehicle Based on Vision Information

Water hazard is one of the most dangerous obstacles for unmanned vehicle to travel in off-road environment. The vision information for water hazard body includes relatively high brightness, low saturation and smooth texture. In this paper, static image and sequence image are captured by CCD. Color and texture information extracted from image pixels would be utilized to form a feature matrix to train the SVM classifier. While, with the assistance of RBF kernel function, low-dimensional sample space is projected to high-dimensional space. Based on large amount of experiments, RBF kernel function parameters are optimized by using grid method. The optimized RBF kernel function parameters are proved satisfactory when detecting in the static water image. Notice that there is certain relationship between target position and its scale for the adjacent frame. SURF feature detection and matching method can be used to match feature point between adjacent frames in image sequences. The searching window size and position in new frame could be updated in time, which allows to detect water hazard in a relatively small region. Water obstacle tracking experiment with patents proved the satisfactory performance of the SURF method in this paper.