title: Adaptive Shape Kernel-Based Mean Shift Tracker in Robot Vision System.
abstract: 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.
authors.affiliation: Department of Computer Science and Technology, Tongji University, Shanghai 201804, China.
authors.forename: Chunmei
authors.identifier: 0000-0002-8973-8217
authors.initials: C
authors.lastname: Liu
authors.affiliation: The College of Information Sciences and Technology, Donghua University, Shanghai 201620, China.
authors.forename: Yirui
authors.identifier: 0000-0001-5767-3343
authors.initials: Y
authors.lastname: Wang
authors.affiliation: Faculty of Engineering, University of Toyama, Toyama 930-8555, Japan.
authors.forename: Shangce
authors.identifier: 0000-0001-5042-3261
authors.initials: S
authors.lastname: Gao
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D008124:Locomotion; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results; D012371:Robotics; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D014741:Video Recording