Oriented projective geometry
Iterative point matching for registration of free-form curves and surfaces
International Journal of Computer Vision
3-D Motion Estimation in Model-Based Facial Image Coding
IEEE Transactions on Pattern Analysis and Machine Intelligence
3-D model-based tracking of humans in action: a multi-view approach
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Twist Based Acquisition and Tracking of Animal and Human Kinematics
International Journal of Computer Vision
Three-Dimensional Shape Knowledge for Joint Image Segmentation and Pose Tracking
International Journal of Computer Vision
Clustered stochastic optimization for object recognition and pose estimation
Proceedings of the 29th DAGM conference on Pattern recognition
Point matching as a classification problem for fast and robust object pose estimation
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
PCA-SIFT: a more distinctive representation for local image descriptors
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
High accuracy optical flow serves 3-d pose tracking: exploiting contour and flow based constraints
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Robust pose estimation with 3d textured models
PSIVT'06 Proceedings of the First Pacific Rim conference on Advances in Image and Video Technology
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In this work, we propose a model-based approach for estimating the 3D position and orientation of a dummy's head for crash test video analysis. Instead of relying on photogrammetric markers which provide only sparse 3D measurements, features present in the texture of the object's surface are used for tracking. In order to handle also small and partially occluded objects, the concepts of region-based and patch-based matching are combined for pose estimation. For a qualitative and quantitative evaluation, the proposed method is applied to two multi-view crash test videos captured by high-speed cameras.