Model-based 3-D object tracking has earned significant importance in areas such as augmented reality, surveillance, visual servoing, robotic object manipulation and grasping. Key problems to robust and precise object tracking are the outliers caused by occlusion, self-occlusion, cluttered background, reflections and complex appearance properties of the object. Two of the most common solutions to the above problems have been the use of robust estimators and the integration of visual cues. The tracking system presented in this paper achieves robustness by integrating model-based and model-free cues together with robust estimators. As a model-based cue, a wireframe edge model is used. As model-free cues, automatically generated surface texture features are used. The particular contribution of this work is the integration framework where not only polyhedral objects are considered. In particular, we deal also with spherical, cylindrical and conical objects for which the complete pose cannot be estimated using only wireframe models. Using the integration with the model-free features, we show how a full pose estimate can be obtained. Experimental evaluation demonstrates robust system performance in realistic settings with highly textured objects and natural backgrounds.
- Cue integration
- Iterated extended Kalman filter
- Model-based tracking
- Model-free tracking