Open AccessInvariant foreground occupation ratio for scale adaptive mean shift tracking
Author(s) -
Song Yi,
Li Shuxiao,
Zhu Chengfei,
Jiang Sheng,
Chang Hongxing
Publication year - 2015
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2014.0150
Subject(s) - mean shift , weighting , artificial intelligence , computer vision , scale invariance , pixel , scale (ratio) , tracking (education) , mathematics , computer science , invariant (physics) , video tracking , pattern recognition (psychology) , algorithm , object (grammar) , statistics , medicine , psychology , pedagogy , physics , quantum mechanics , mathematical physics , radiology
The mean shift algorithm has been introduced successfully into the field of computer vision to be an efficient approach for visual tracking but the tracker has been awkward in handling the scale change of the object. This study addresses the scale estimation problem of the mean shift tracker, and proposes a novel method which is based on invariant foreground occupation ratio to solve this problem. The foreground occupation ratio is defined as the proportion of the foreground pixels in an image region. By taking an analysis of the foreground occupation ratio, the authors obtain its three simple properties. With its property of scale invariance, an iterative approximation approach is employed to estimate the scale of the foreground in the current image. The scale value is modified by a weighting function, and it is adjusted along the two axes with respect to the width and the height of the target. The scale estimation algorithm is then employed in the mean shift tracker to obtain the ability of scale adaptation for tracking. Experimental results show that, using the authors method for object scale estimation, the mean shift tracker performs well in tracking the target efficiently when its scale continuously changes.