Scale-invariant object tracking method using strong corners in the scale domain

Abstract

The object tracking method using the scale-invariant feature transform (SIFT) is applicable to rotated or scaled targets, and also maintains good performance in occluded or intensity-changed images. However, the SIFT algorithm has high computational complexity. In addition, the template size has to be sufficiently large to extract enough features to match. This paper proposes a scale-invariant object tracking method using strong corner points in the scale domain. The proposed method makes it possible to track a smaller object than the SIFT tracker by extracting relatively more features from a target image. In the proposed method, strong features of the template image, which correspond to strong corner points in the scale domain, are selected. The strong features of the template image are then matched with all features of the target image. The matched features are used to find relations between the template and target images. In experimental results, the proposed method shows better performance than the existing SIFT tracker. (C) 2009 Society of Photo-Optical Instrumentation Engineers. [DOI: 10.1117/1.3070665]