Abstract
This paper tackles the problem of 3D object pose tracking from monocular cameras. Data association is performed via a variant of the Iterative Closest Point algorithm, thus making it robust to noise and other artifacts. We re-initialise the hypothesis space based on the resulting re-projection error between hypothesised models and observed image objects. This is performed through a non-linear minimisation step after correspondences are found. The use of multi-hypotheses and correspondences refinement, lead to a robust framework. Experimental results with benchmark image sequences indicate the effectiveness of our framework.
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Chliveros, G., Pateraki, M., Trahanias, P. (2013). Robust Multi-hypothesis 3D Object Pose Tracking. In: Chen, M., Leibe, B., Neumann, B. (eds) Computer Vision Systems. ICVS 2013. Lecture Notes in Computer Science, vol 7963. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39402-7_24
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DOI: https://doi.org/10.1007/978-3-642-39402-7_24
Publisher Name: Springer, Berlin, Heidelberg
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