High-Resolution Terrain Map from Multiple Sensor Data
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Accurate and Efficient Stereo Processing by Semi-Global Matching and Mutual Information
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Geometric Context from a Single Image
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Minimizing Nonsubmodular Functions with Graph Cuts-A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Stixel World - A Compact Medium Level Representation of the 3D-World
Proceedings of the 31st DAGM Symposium on Pattern Recognition
A Real-Time Low-Power Stereo Vision Engine Using Semi-Global Matching
ICVS '09 Proceedings of the 7th International Conference on Computer Vision Systems: Computer Vision Systems
Order-Preserving Moves for Graph-Cut-Based Optimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
3D reconstruction using an n-layer heightmap
Proceedings of the 32nd DAGM conference on Pattern recognition
6D-vision: fusion of stereo and motion for robust environment perception
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
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Dense stereo vision has evolved into a powerful foundation for the next generation of intelligent vehicles. The high spatial and temporal resolution allows for robust obstacle detection in complex inner city scenarios, including pedestrian recognition and detection of partially hidden moving objects. Aiming at a vision architecture for efficiently solving an increasing number of vision tasks, the medium-level representation named Stixel World has been developed. This paper shows how this representation forms the foundation for a very efficient, robust and comprehensive understanding of traffic scenes. A recently proposed Stixel computation scheme allows the extraction of multiple objects per image column and generates a segmentation of the input data. The motion of the Stixels is obtained by applying the 6D-Vision principle to track Stixels over time. Subsequently, this allows for an optimal Stixel grouping such that all dynamic objects can be detected easily. Pose and motion of moving Stixel groups are used to initialize more specific object trackers. Moreover, appearance-based object recognition highly benefits from the attention control offered by the Stixel World, both in performance and efficiency.