Monocular Rear-View Obstacle Detection Using Residual Flow

Jose Molineros¹, Shinko Y. Cheng¹, Yuri Owechko¹, Dan Levi², and Wende Zhang³

¹HRL Laboratories, LLC, 3011 Malibu Canyon Road, Malibu, CA 90265, USA
jmmolineros@hrl.com
sycheng@hrl.com
yowechko@hrl.com

²GM Advanced Technology Center, Israel
dan.levi@gm.com

³GM Research, USA
wende.zhang@gm.com

Abstract. We present a system for automatically detecting obstacles from a moving vehicle using a monocular wide angle camera. Our system was developed in the context of finding obstacles and particularly children when backing up. Camera viewpoint is transformed to a virtual bird-eye view. We developed a novel image registration algorithm to obtain ego-motion that in combination with variational dense optical flow outputs a residual motion map with respect to the ground. The residual motion map is used to identify and segment 3D and moving objects. Our main contribution is the feature-based image registration algorithm that is able to separate and obtain ground layer ego-motion accurately even in cases of ground covering only 20% of the image, outperforming RANSAC.

LNCS 7584, p. 504 ff.

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