In this paper, we address the problem of object tracking in intensity images and depth data. We propose a generic framework that can be used either for tracking 2D templates in intensity images or for tracking 3D objects in depth images. To overcome problems like partial occlusions, strong illumination changes and motion blur, that notoriously make energy minimization-based tracking methods get trapped in a local minimum, we propose a learning-based method that is robust to all these problems. We use random forests to learn the relation between the parameters that defines the object’s motion, and the changes they induce on the image intensities or the point cloud of the template. It follows that, to track the template when it moves, we use the changes on the image intensities or point cloud to predict the parameters of this motion. Our algorithm has an extremely fast tracking performance running at less than 2 ms per frame, and is robust to partial occlusions. Moreover, it demonstrates robustness to strong illumination changes when tracking templates using intensity images, and robustness in tracking 3D objects from arbitrary viewpoints even in the presence of motion blur that causes missing or erroneous data in depth images. Extensive experimental evaluation and comparison to the related approaches strongly demonstrates the benefits of our method.
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In this paper, we address the problem of object tracking in intensity images and depth data. We propose a generic framework that can be used either for tracking 2D templates in intensity images or for tracking 3D objects in depth images. To overcome problems like partial occlusions, strong illumination changes and motion blur, that notoriously make energy minimization-based tracking methods get trapped in a local minimum, we propose a learning-based method that is robust to all these problems. W...
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