Open AccessA Scale Adaptive Mean-Shift Tracking Algorithm for Robot Vision
Author(s) -
Young Jin Kang,
Wandong Xie,
Bin Hu
Publication year - 2013
Publication title -
advances in mechanical engineering/advances in mechanical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.318
H-Index - 40
eISSN - 1687-8140
pISSN - 1687-8132
DOI - 10.1155/2013/601612
Subject(s) - mean shift , robustness (evolution) , affine transformation , computer vision , tracking (education) , artificial intelligence , computer science , algorithm , robot , scale (ratio) , eye tracking , mathematics , pattern recognition (psychology) , psychology , pedagogy , biochemistry , chemistry , physics , quantum mechanics , pure mathematics , gene
The Mean-Shift (MS) tracking algorithm is an efficient tracking algorithm. However, it does not work very well when the scale of a tracking target changes, or targets are occluded in the movements. In this paper, we propose a scale-adaptive Mean-Shift tracking algorithm (SAMSHIFT) to solve these problems. In SAMSHIFT, the corner matching is employed to calculate the affine structure between adjacent frames. The scaling factors are obtained based on the affine structure. Three target candidates, generated by the affine transformation, the Mean Shift and the Mean Shift with resizing by the scaling factors, respectively, are applied in each iteration to improve the tracking performance. By selecting the best candidate among the three, we can effectively improve the scale adaption and the robustness to occlusion. We have evaluated our algorithm in a PC and a mobile robot. The experimental results show that SAMSHIFT is well adaptive to scale changing and robust to partial occlusion, and the tracking speed is fast enough for real-time tracking applications in robot vision