Least-Squares Estimation of Transformation Parameters Between Two Point Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
MLESAC: a new robust estimator with application to estimating image geometry
Computer Vision and Image Understanding - Special issue on robusst statistical techniques in image understanding
A minimum spanning tree algorithm with inverse-Ackermann type complexity
Journal of the ACM (JACM)
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
StereoBox: a robust and efficient solution for automotive short-range obstacle detection
EURASIP Journal on Embedded Systems
Part-based feature synthesis for human detection
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Variational optical flow computation in real time
IEEE Transactions on Image Processing
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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.