A moving object recognition method by optical flow analysis

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