Assessing the performance of corner detectors for point feature tracking applications

Abstract In this paper we assess the performance of a variety of corner (point) detecting algorithms for feature tracking applications. We analyze four different types of corner extractors, which have been widely used for a variety of applications (they are described later in the paper). We use corner stability and corner localization properties as measures to evaluate the quality of the features extracted by the four detectors. For effective assessment of the corner detectors, first, we employed image sequences with no motion (simply static image sequences), so that the appearance and disappearance of corners in each frame is purely due to image plane noise and illumination conditions. The second stage included experiments on sequences with small motion. The experiments were devised to make the testing environment ideal to analyze the stability and localization properties of the corners extracted. The corners detected from the initial frame are then matched through the sequence using a corner matching strategy. We employed two different types of matchers, namely the GVM (Gradient Vector Matcher) and the Product Moment Coefficient Matcher (PMCM). Each of the corner detectors was tested with each of the matching algorithms to evaluate their performance in tracking (matching) the features. The experiments were carried out on a variety of image sequences with and without motion.

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