3D Pose Estimation by Directly Matching Polyhedral Models to Gray Value Gradients
International Journal of Computer Vision
International Journal of Computer Vision
Tracking with the EM Contour Algorithm
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
International Journal of Computer Vision
A Framework for Model-Based Tracking Experiments in Image Sequences
International Journal of Computer Vision
Model-Based Tracking by Classification in a Tiny Discrete Pose Space
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hybrid tracking algorithms for planar and non-planar structures subject to illumination changes
ISMAR '06 Proceedings of the 5th IEEE and ACM International Symposium on Mixed and Augmented Reality
Contour tracking based on marginalized likelihood ratios
Image and Vision Computing
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Addresses the problem of pose estimation and tracking of vehicles in image sequences from traffic scenes recorded by a stationary camera. In a new algorithm, the vehicle pose is estimated by directly fitting image gradients to polyhedral vehicle models without an edge segment extraction process. The new approach is significantly more robust than approaches that rely on feature extraction because the new approach exploits more information from the image data. We can track vehicles that are partially occluded by textured objects, e.g. foliage, where classical approaches based on edge segment extraction fail. Results from various experiments with real-world traffic scenes are presented.