Abstract
—This paper presents a new idea for an obstacle recognition method for mobile robots by analyzing optical flow information acquired from dynamic images. 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 a normalized square residual error [focus of expansion (FOE) residual error] value that is calculated in the process of estimating the FOE. Next, the optical flow directions and intensity values are stored for the pixels involved in each candidate region to calculate the distribution width values around the principal axes of inertia and the direction of the principal axes. Finally, each candidate is classified into an object category that is expected to appear in the scene by comparing the proportion and the direction values with standard data ranges for the objects which are determined by preliminary experiments. Experimental results of car/bicycle/pedestrian recognition in real outdoor scenes have shown the effectiveness of the proposed method.
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