Non-parametric local transforms for computing visual correspondence
ECCV '94 Proceedings of the third European conference on Computer Vision (Vol. II)
A technique for counting ones in a binary computer
Communications of the ACM
Digital Geometry: Geometric Methods for Digital Picture Analysis
Digital Geometry: Geometric Methods for Digital Picture Analysis
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
Efficient Dense Scene Flow from Sparse or Dense Stereo Data
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Evaluation of Stereo Matching Costs on Images with Radiometric Differences
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
Detection and Segmentation of Independently Moving Objects from Dense Scene Flow
EMMCVPR '09 Proceedings of the 7th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
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
Probabilistic multi-class scene flow segmentation for traffic scenes
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
Stereo- and neural network-based pedestrian detection
IEEE Transactions on Intelligent Transportation Systems
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This paper presents a novel way of combining dense stereo and motion analysis for the purpose of mid-level scene segmentation and object tracking. The input is video data that addresses long-range stereo analysis, as typical when recording traffic scenes from a mobile platform. The task is to identify shapes of traffic-relevant objects without aiming at object classification at the considered stage. We analyse disparity dynamics in recorded scenes for solving this task. Statistical shape models are generated over subsequent frames. Shape correspondences are established by using a similarity measure based on set theory. The motion of detected shapes (frame to frame) is compensated by using a dense motion field as produced by a real-time optical flow algorithm. Experimental results show the quality of the proposed method which is fairly simple to implement.