Model-based object tracking in monocular image sequences of road traffic scenes
International Journal of Computer Vision
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WACV '02 Proceedings of the Sixth IEEE Workshop on Applications of Computer Vision
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CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
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International Journal of Computer Vision
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International Journal of Computer Vision
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IEEE Transactions on Intelligent Transportation Systems
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IEEE Transactions on Intelligent Transportation Systems
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IEEE Transactions on Intelligent Transportation Systems
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IEEE Transactions on Intelligent Transportation Systems
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IEEE Transactions on Intelligent Transportation Systems
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IEEE Transactions on Intelligent Transportation Systems
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IEEE Transactions on Intelligent Transportation Systems
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IEEE Transactions on Intelligent Transportation Systems
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IEEE Transactions on Intelligent Transportation Systems
Expandable Bayesian networks for 3D object description from multiple views and multiple mode inputs
IEEE Transactions on Pattern Analysis and Machine Intelligence
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Model-based temporal object verification using video
IEEE Transactions on Image Processing
3-D model-based vehicle tracking
IEEE Transactions on Image Processing
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In this paper, we present a new generic model-based approach for building 3-D models of vehicles from color video from a single uncalibrated traffic-surveillance camera. We propose a novel directional template method that uses trigonometric relations of the 2-D features and geometric relations of a single 3-D generic vehicle model to map 2-D features to 3-D in the face of projection and foreshortening effects.We use novel hierarchical structural similarity measures to evaluate these single-frame-based 3-D estimates with respect to the generic vehicle model. Using these similarities, we adopt a weighted clustering technique to build a 3-D model of the vehicle for the current frame. The 3-D features are then adaptively clustered again over the frame sequence to generate an incremental 3-D model of the vehicle. Results are shown for several simulated and real traffic videos in an uncontrolled setup. Finally, the results are evaluated by the same structural performance measure, underscoring the usefulness of incremental learning. The performance of the proposed method for several types of vehicles in two considerably different traffic spots is very promising to encourage its applicability in 3-D reconstruction of other rigid objects in video.