Keywords: Computer Vision
We propose a temporal tracking algorithm based on Random Forest that uses depth images to estimate and track the 3D pose of a rigid object in real-time. Compared to the state of the art aimed at the same goal, our algorithm holds important attributes such as high robustness against holes and occlusion, low computational cost of both learning and tracking stages, and low memory consumption. Due to these attributes, we report state-of-the-art tracking accuracy on benchmark datasets, and accomplish remarkable scalability with the number of targets, being able to simultaneously track the pose of over a hundred objects at 30 fps with an off-the-shelf CPU. In addition, the fast learning time enables us to extend our algorithm as a robust online tracker for model-free 3D objects under different viewpoints.
|| D. J. Tan , F. Tombari, S. Ilic, N. Navab
A Versatile Learning-based 3D Temporal Tracker: Scalable, Robust, Online
International Conference on Computer Vision (ICCV), Santiago, Chile, December 2015
|| D. J. Tan , S. Ilic
Multi-Forest Tracker: A Chameleon in Tracking
IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, USA, June 24, 2014
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