Authors:
Hongpeng Yin
1
;
Yi Chai
1
;
Simon X. Yang
2
and
David K. Y. Chiu
3
Affiliations:
1
Chongqing University, China
;
2
School of Engineering, University of Guelph, Canada
;
3
Dept. of Computing and Information Science, Univ. of Guelph, Canada
Keyword(s):
Target tracking, Feature fusion, Template update, Kernel-based tracking.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Decision Support Systems
;
Feature Extraction
;
Features Extraction
;
Image and Video Analysis
;
Image Processing
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Robotics and Automation
;
Signal Processing, Sensors, Systems Modeling and Control
Abstract:
This paper presents an online adaptive multiple feature fusion and template update mechanism for kernel-based target tracking. According to the discrimination between the object and background, measured by two-class variance ratio, the multiple features are combined by linear weighting to realize kernel-based tracking. An adaptive model-updating mechanism based on the likelihood of the features between successive frames is addressed to alleviate the mode drifts. In this paper, RGB colour features, Prewitt edge feature and local binary pattern (LBP) texture feature are employed to implement the scheme. Experiments on several video sequences show the effectiveness of the proposed method.