Passing vehicle detection from dynamic background using robust information fusion
This work presents a robust method of passing vehicle detection. Obstacle detection algorithms that rely on motion estimation tend to be sensitive to image outliers caused by structured noise and shadows. To achieve a reliable vision system, we have developed two important techniques, motion estimation with robust information fusion and dynamic scene modeling. By exploiting the uncertainty of flow estimates, our information fusion scheme gives robust estimation of image motion. In addition, we also model the background and foreground dynamics of road scenes and impose coherency constraints to eliminate outliers. The proposed detection scheme is used by a single-camera vision system developed for driver assistance. Our test results have shown superior performance achieved by the new detection method.