CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
A Theory of Single-Viewpoint Catadioptric Image Formation
International Journal of Computer Vision
A Unifying Theory for Central Panoramic Systems and Practical Applications
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Catadioptric Sensors that Approximate Wide-Angle Perspective Projections
OMNIVIS '00 Proceedings of the IEEE Workshop on Omnidirectional Vision
Constant Resolution Omnidirectional Cameras
OMNIVIS '02 Proceedings of the Third Workshop on Omnidirectional Vision
Panoramic Mosaicing with a 180° Field of View Lens
OMNIVIS '02 Proceedings of the Third Workshop on Omnidirectional Vision
Robust Visual Tracking by Integrating Multiple Cues Based on Co-Inference Learning
International Journal of Computer Vision - Special Issue on Computer Vision Research at the Beckman Institute of Advanced Science and Technology
Fast Multiple Object Tracking via a Hierarchical Particle Filter
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Monocular model-based 3D tracking of rigid objects
Foundations and Trends® in Computer Graphics and Vision
Real-Time Camera Tracking Using Known 3D Models and a Particle Filter
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Human Tracking by Particle Filtering Using Full 3D Model of Both Target and Environment
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
A Rao-Blackwellized particle filter for EigenTracking
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Estimation of omnidirectional camera model from epipolar geometry
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Learning Object Affordances: From Sensory--Motor Coordination to Imitation
IEEE Transactions on Robotics
Robust Shape Tracking With Multiple Models in Ultrasound Images
IEEE Transactions on Image Processing
Comparison of stochastic filtering methods for 3D tracking
Pattern Recognition
Multi-robot cooperative spherical-object tracking in 3D space based on particle filters
Robotics and Autonomous Systems
Robotics and Autonomous Systems
| Hi-index | 0.00 |
We present a color and shape based 3D tracking system suited to a large class of vision sensors. The method is applicable, in principle, to any known calibrated projection model. The tracking architecture is based on particle filtering methods where each particle represents the 3D state of the object, rather than its state in the image, therefore overcoming the nonlinearity caused by the projection model. This allows the use of realistic 3D motion models and easy incorporation of self-motion measurements. All nonlinearities are concentrated in the observation model so that each particle projects a few tens of special points onto the image, on (and around) the 3D object's surface. The likelihood of each state is then evaluated by comparing the color distributions inside and outside the object's occluding contour. Since only pixel access operations are required, the method does not require the use of image processing routines like edge/feature extraction, color segmentation or 3D reconstruction, which can be sensitive to motion blur and optical distortions typical in applications of omnidirectional sensors to robotics. We show tracking applications considering different objects (balls, boxes), several projection models (catadioptric, dioptric, perspective) and several challenging scenarios (clutter, occlusion, illumination changes, motion and optical blur). We compare our methodology against a state-of-the-art alternative, both in realistic tracking sequences and with ground truth generated data.