Image gradient evolution-a visual cue for collision avoidance

Hongche Liu, Tsai-Hong Hong, Martin Herman, Rama Chellappa

Abstract: This paper is concerned with the task of visual motion-based navigation. A critical requirement of the task is the ability to estimate 3D depth and motion from visual information. Recent studies have demonstrated that the relevant cues are contained in motion parallax or optical flow from which flow field divergence and hence time-to-contact can be extracted. We present a new concept called image gradient evolution, which utilizes the change of image spatial gradients over time as a threat cue; an approaching object induces 2D expanding motion and causes the image spatial structure to stretch so the image gradients decrease. Based on this idea, other method offers a one-step solution directly from image gradients, instead of optical flow and its derived properties. We use a technique that is local and linear so the implementation can be very fast. The threat map is expectedly noisy but sufficiently informative. As seen in demonstrations on several real images these two aspects, fast implementation and useful qualitative information, provide a viable solution to navigational tasks.