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Extending GKLT tracking - feature tracking for controlled enviroments with integrated uncertainty estimation

: Trummer, M.; Dunzler, J.; Munkelt, C.


Salberg, A.-B. ; International Association for Pattern Recognition -IAPR-:
Image analysis. 16th Scandinavian Conference, SCIA 2009 : Oslo, Norway, June 15 - 18, 2009; proceedings
Berlin: Springer, 2009 (Lecture Notes in Computer Science 5575)
ISBN: 978-3-642-02229-6
ISSN: 0302-9743
Scandinavian Conference on Image Analysis (SCIA) <16, 2009, Oslo>
Conference Paper
Fraunhofer IOF ()

Guided Kanade-Lucas-Tomasi (GKLT) feature tracking offers a way to perform KLT tracking for rigid scenes using known camera parameters as prior knowledge, but requires manual control of uncertainty. The uncertainty of prior knowledge is unknown in general. We present an extended modeling of GKLT that overcomes the need of manual adjustment of the uncertainty parameter. We establish an extended optimization error function for GKLT feature tracking, from which we derive extended parameter update rules and a new optimization algorithm in the context of KLT tracking. By this means we give a new formulation of KLT tracking using known camera parameters originating, for instance, from a controlled environment. We compare the extended GKLT tracking method with the original GKLT and the standard KLT tracking using real data. The experiments show that the extended GKLT tracking performs better than the standard KLT and reaches an accuracy up to several times better than the original GKLT with an improperly chosen value of the uncertainty parameter.