Adaptive Shape Kernel-Based Mean Shift Tracker in Robot Vision System
Joint Authors
Wang, Yirui
Gao, Shangce
Liu, Chunmei
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2016, Issue 2016 (31 Dec. 2015), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-06-09
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
This paper proposes an adaptive shape kernel-based mean shift tracker using a single static camera for the robot vision system.
The question that we address in this paper is how to construct such a kernel shape that is adaptive to the object shape.
We perform nonlinear manifold learning technique to obtain the low-dimensional shape space which is trained by training data with the same view as the tracking video.
The proposed kernel searches the shape in the low-dimensional shape space obtained by nonlinear manifold learning technique and constructs the adaptive kernel shape in the high-dimensional shape space.
It can improve mean shift tracker performance to track object position and object contour and avoid the background clutter.
In the experimental part, we take the walking human as example to validate that our method is accurate and robust to track human position and describe human contour.
American Psychological Association (APA)
Liu, Chunmei& Wang, Yirui& Gao, Shangce. 2016. Adaptive Shape Kernel-Based Mean Shift Tracker in Robot Vision System. Computational Intelligence and Neuroscience،Vol. 2016, no. 2016, pp.1-8.
https://search.emarefa.net/detail/BIM-1099716
Modern Language Association (MLA)
Liu, Chunmei…[et al.]. Adaptive Shape Kernel-Based Mean Shift Tracker in Robot Vision System. Computational Intelligence and Neuroscience Vol. 2016, no. 2016 (2015), pp.1-8.
https://search.emarefa.net/detail/BIM-1099716
American Medical Association (AMA)
Liu, Chunmei& Wang, Yirui& Gao, Shangce. Adaptive Shape Kernel-Based Mean Shift Tracker in Robot Vision System. Computational Intelligence and Neuroscience. 2016. Vol. 2016, no. 2016, pp.1-8.
https://search.emarefa.net/detail/BIM-1099716
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1099716