An Optical Flow Based Time-to-Collision Predictor

This paper describes a new method for estimating time-to-collision which exhibits high tolerance to noise contained in camera images. Time to collision (TTC) is one of the most important parameters available from a camera attached to a mobile machine. TTC indirectly stands far the translation speed of the camera and is usually calculated either from successive images or optical flow by using intimate relationship between TTC and flow divergence. In most cases, however, it is not easy to get accurate optical flow, which makes it difficult to calculate TTC. In this study it is proved that if the target has a smooth surface, the average of divergence over any point-symmetric region on the image is equal to the divergence of the center of the region. It means that required divergence can be calculated by integrating optical flow vectors over a symmetric region. It is expected that in the process of the integration, accidental noise is canceled if they are independent of optical flow and the motion of the camera. Experimental results show that TTC can be estimated regardless of the surface condition. It is also shown that influence of noise is eliminated as the area of integration increases.