This paper presents a new method for estimating locations of moving obstacles from stereo images without tracking between image frames and without searching corresponding points between stereo images. First, a free-space map in front of a vehicle is created by the planar projection stereopsis method which can easily decide whether or not each point in a stereo image exists on a ground plane. Next, the moving obstacles detected in an image by using the residual error calculated in the process of focus of expansion estimation are projected to the free-space map. In the free space map, obstacle locations are estimated as the intersection between the projected obstacle areas and the contour of a free-space area. Experimental results for real road scenes with pedestrian, bicycle or automobile as moving obstacles show the effectiveness of the proposed method.

This paper presents a new method which can effectively recognize moving objects by analyzing optical flow information acquired from dynamic images. This MOROFA (moving object recognition by optical flow analysis) method can be applied to many industrial areas; for example, an intelligent machine surveillance system or an obstacle detection system for an autonomous vehicle. At first, the optical flow field is detected in image sequences from a camera on a moving observer and moving object candidates are extracted by using the residual error value that is calculated in the process of estimating the focus of expansion. Next, the optical flow directions and intensity values are stored for the pixels involved in each candidate region to calculate the directions and the proportion values of the principal components. Finally, each candidate is classified into a category of object that is expected to appear in the scene by comparing the direction and the proportion values with standard data ranges for the objects which are determined by preliminary experiments. Experimental results of real outdoor scenes have shown the effectiveness of the proposed method.

The paper presents a simple moving obstacle detection method which used the residual error calculated in uses of focus of expansion (FOE) estimation. Method can be applied to many industrial tasks such as an intelligent machine surveillance system or an obstacle detection system for an autonomous vehicle system. First, the optical flow field is extracted from sequence of dynamic imaged captured by a fixed camera on a moving observer. Next, the FOE is estimated in local image regions. Its residual error is first calculated in the region. An image region corresponding to the block is added with the residual error. This process is repeated by sliding and adding for the local region, and changing the size of the local region. Finally, regions which have high residual error values are detected as candidate regions of moving obstacles. Experimental results using real outdoor scenes show the effectiveness of the proposed method.

This paper presents a new road extraction method for an autonomous vehicle which can acquire a road area by using height information of objects. Since a road area can be assumed to be a sequence of a flat plane in front of a vehicle, the road's height information is very effective for extracting a road area. For the purpose, the authors propose a new approach named the planar projection stereopsis (PPS) method which can easily decide whether each point in stereo images exists on the road plane or not. At first, PPS calculates a planar equation representing a road area by using height and pose of a camera in the vehicle. Next, stereo images are projected to the plane, where corresponding points are projected to the same positions on a certain road area if they really exist on a road plane, while corresponding points with different heights from the road plane are projected to different positions in each stereo image. Planar projection description is obtained by a subtraction between projected images from a set of stereo images and a road area can be represented by a set of points with small values. Experimental results for real road scenes have shown the effectiveness of the proposed method.