Multiple constraints for optical flow
ECCV '94 Proceedings of the third European conference on Computer vision (vol. 1)
Algorithmic modelling for performance evaluation
Machine Vision and Applications - Special issue on performance evaluation
Motion Segmentation with Census Transform
PCM '01 Proceedings of the Second IEEE Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Nonlinear Matrix Diffusion for Optic Flow Estimation
Proceedings of the 24th DAGM Symposium on Pattern Recognition
Performance Evaluation of Object Detection Algorithms
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Vision-Based SLAM: Stereo and Monocular Approaches
International Journal of Computer Vision
Generating Ground Truthed Dataset of Chart Images: Automatic or Semi-automatic?
Graphics Recognition. Recent Advances and New Opportunities
Approximated Ground Truth for Stereo and Motion Analysis on Real-World Sequences
PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
Smoothed Disparity Maps for Continuous American Sign Language Recognition
IbPRIA '09 Proceedings of the 4th Iberian Conference on Pattern Recognition and Image Analysis
The Stixel World - A Compact Medium Level Representation of the 3D-World
Proceedings of the 31st DAGM Symposium on Pattern Recognition
Simultaneous Estimation of Pose and Motion at Highly Dynamic Turn Maneuvers
Proceedings of the 31st DAGM Symposium on Pattern Recognition
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
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
Performance Evaluation of Stereo Algorithms for Automotive Applications
ICVS '09 Proceedings of the 7th International Conference on Computer Vision Systems: Computer Vision Systems
A robust approach for ego-motion estimation using a mobile stereo platform
IWCM'04 Proceedings of the 1st international conference on Complex motion
A duality based approach for realtime TV-L1 optical flow
Proceedings of the 29th DAGM conference on Pattern recognition
The 2005 PASCAL visual object classes challenge
MLCW'05 Proceedings of the First international conference on Machine Learning Challenges: evaluating Predictive Uncertainty Visual Object Classification, and Recognizing Textual Entailment
6D-vision: fusion of stereo and motion for robust environment perception
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
Performance evaluation of object detection and tracking in video
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
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The accuracy of stereo algorithms or optical flow methods is commonly assessed by comparing the results against the Middlebury database. However, equivalent data for automotive or robotics applications rarely exist as they are difficult to obtain. As our main contribution, we introduce an evaluation framework tailored for stereo-based driver assistance able to deliver excellent performance measures while circumventing manual label effort. Within this framework one can combine several ways of ground-truthing, different comparison metrics, and use large image databases. Using our framework we show examples on several types of ground-truthing techniques: implicit ground truthing (e.g. sequence recorded without a crash occurred), robotic vehicles with high precision sensors, and to a small extent, manual labeling. To show the effectiveness of our evaluation framework we compare three different stereo algorithms on pixel and object level. In more detail we evaluate an intermediate representation called the Stixel World. Besides evaluating the accuracy of the Stixels, we investigate the completeness (equivalent to the detection rate) of the Stixel World vs. the number of phantom Stixels. Among many findings, using this framework enables us to reduce the number of phantom Stixels by a factor of three compared to the base parametrization. This base parametrization has already been optimized by test driving vehicles for distances exceeding 10000 km.