Efficient Region Tracking With Parametric Models of Geometry and Illumination
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bi-Layer Segmentation of Binocular Stereo Video
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Bilayer Segmentation of Live Video
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Near real-time motion segmentation using graph cuts
DAGM'06 Proceedings of the 28th conference on Pattern Recognition
Realtime depth estimation and obstacle detection from monocular video
DAGM'06 Proceedings of the 28th conference on Pattern Recognition
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Space-Time Multi-Resolution Banded Graph-Cut for Fast Segmentation
Proceedings of the 30th DAGM symposium on Pattern Recognition
Moving Object Segmentation Using Optical Flow and Depth Information
PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
A Simple On-Road Object Segmentation Approach in ITS System
CAR '09 Proceedings of the 2009 International Asia Conference on Informatics in Control, Automation and Robotics
3D information extraction using Region-based Deformable Net for monocular robot navigation
Journal of Visual Communication and Image Representation
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Autonomous collision avoidance in vehicles requires an accurate separation of obstacles from the background, particularly near the focus of expansion. In this paper, we present a technique for fast segmentation of stationary obstacles from video recorded by a single camera that is installed in a moving vehicle. The input image is divided into three motion segments consisting of the ground plane, the background, and the obstacle. This constrained scenario allows for good initial estimates of the motion models, which are iteratively refined during segmentation. The horizon is known due to the camera setup. The remaining binary partitioning problem is solved by a graph cut on the motion-compensated difference images. Obstacle segmentation in realistic scenes with a monocular camera setup has not been feasible up to now. Our experimental evaluation shows that the proposed approach leads to fast and accurate obstacle segmentation and distance estimation without prior knowledge about the size, shape or base point of obstacles.