Model-based object tracking in monocular image sequences of road traffic scenes
International Journal of Computer Vision
Real-Time Tracking Using Wavelet Representation
Proceedings of the 24th DAGM Symposium on Pattern Recognition
Lucas-Kanade 20 Years On: A Unifying Framework
International Journal of Computer Vision
Level Set Evolution without Re-Initialization: A New Variational Formulation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
International Journal of Computer Vision
Multi-cue Pedestrian Detection and Tracking from a Moving Vehicle
International Journal of Computer Vision
Coupled Object Detection and Tracking from Static Cameras and Moving Vehicles
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Real-Time Visual Tracking Using Pixel-Wise Posteriors
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Robust Multiperson Tracking from a Mobile Platform
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Combinatorial Solution for Model-Based Image Segmentation and Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Three-dimensional shape knowledge for joint image segmentation and pose estimation
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
Multiregion level set tracking with transformation invariant shape priors
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
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We propose a new approach for integrating geometric scene knowledge into a level-set tracking framework. Our approach is based on a novel constrained-homography transformation model that restricts the deformation space to physically plausible rigid motion on the ground plane. This model is especially suitable for tracking vehicles in automotive scenarios. Apart from reducing the number of parameters in the estimation, the 3D transformation model allows us to obtain additional information about the tracked objects and to recover their detailed 3D motion and orientation at every time step. We demonstrate how this information can be used to improve a Kalman filter estimate of the tracked vehicle dynamics in a higher-level tracker, leading to more accurate object trajectories. We show the feasibility of this approach for an application of tracking cars in an inner-city scenario.