Authors:
Tobias Senst
;
Brigitte Unger
;
Ivo Keller
and
Thomas Sikora
Affiliation:
Technische Universität Berlin, Germany
Keyword(s):
Feature detector, Feature detection Performance evaluation, Tracking, KLT.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Feature Selection and Extraction
;
Human-Computer Interaction
;
Image and Video Analysis
;
Methodologies and Methods
;
Motion and Tracking
;
Motion, Tracking and Stereo Vision
;
Pattern Recognition
;
Physiological Computing Systems
;
Software Engineering
;
Theory and Methods
;
Video Analysis
Abstract:
Due to its high computational efficiency the Kanade Lucas Tomasi feature tracker is still widely accepted and a utilized method to compute sparse motion fields or trajectories in video sequences. This method is made up 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 takes into account 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 like 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.