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Performance Evaluation of Feature Detection for Local Optical Flow Tracking
Citation key 1335Senst2012
Author Tobias Senst and Brigitte Unger and Ivo Keller and Thomas Sikora
Title of Book International Conference on Pattern Recognition Applications and Methods (ICPRAM 2012)
Pages 303–309
Year 2012
Address Vilamoura, Portugal
Month feb
Note DOI: 10.5220/0003731103030309
Abstract Due to its high computational efficiency the Kanade Lucas Tomasi feature tracker still remains as a widely accepted and utilized method to compute sparse motion fields or trajectories in video sequences. This method consists of a Good Feature To Track feature detection and a pyramidal Lucas Kanade feature tracking algorithm. It is well known that the Good Feature To Track concerns the Aperture Problem, but it does not consider the Generalized Aperture Problem. In this paper we want to provide an evaluation of a set of alternative feature detection methods. These methods are taken from feature matching techniques as FAST, SIFT and MSER. The evaluation is based on the Middlebury dataset and performed by using an improved pyramidal Lucas Kanade method, called RLOF feature tracker. To compare the results of the feature detector and RLOF pair, we propose a methodology based on accuracy, efficiency and covering measurements.
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