Abstract
This work investigates the problem of 6-Degrees-Of-Freedom (6-DOF) object tracking from RGB-D images, where the object is rigid and a 3D model of the object is known. As in many previous works, we utilize a Particle Filter (PF) framework. In order to have a fast tracker, the key aspect is to design a clever proposal distribution which works reliably even with a small number of particles. To achieve this we build on a recently developed state-of-the-art system for single image 6D pose estimation of known 3D objects, using the concept of so-called 3D object coordinates. The idea is to train a random forest that regresses the 3D object coordinates from the RGB-D image. Our key technical contribution is a two-way procedure to integrate the random forest predictions in the proposal distribution generation. This has many practical advantages, in particular better generalization ability with respect to occlusions, changes in lighting and fast-moving objects. We demonstrate experimentally that we exceed state-of-the-art on a given, public dataset. To raise the bar in terms of fast-moving objects and object occlusions, we also create a new dataset, which will be made publicly available.
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Notes
- 1.
The group of rigid body transformations.
- 2.
Please note, that because of its circular nature, applying rotations with the normally distributed angles \(\theta \) will result in angles distributed in the interval between \(0\) and \(2 \pi \) according to a wrapped normal distribution. Such a distribution is difficult to handle and we will use a von Mises distribution as approximation.
- 3.
Direction and length of a rotation vector correspond to rotation axis and rotation angle, respectively.
- 4.
Intel Core i7-3820 CPU @ 3.6GHz with a Nvidia GTX 550 TI GPU.
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Acknowledgement
This work has partially been supported by the European Social Fund and the Federal State of Saxony within project VICCI (#100098171).
We thank Daniel Schemala for development of the manual pose annotation tool, we used to generate ground truth data.
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Krull, A., Michel, F., Brachmann, E., Gumhold, S., Ihrke, S., Rother, C. (2015). 6-DOF Model Based Tracking via Object Coordinate Regression. In: Cremers, D., Reid, I., Saito, H., Yang, MH. (eds) Computer Vision -- ACCV 2014. ACCV 2014. Lecture Notes in Computer Science(), vol 9006. Springer, Cham. https://doi.org/10.1007/978-3-319-16817-3_25
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