Open AccessA Comparative Analysis of Kernel-Based Target Tracking Methods using Different Colour Feature Based Target Models
Author(s) -
Nirja Magoch Thakur,
MM Kuber
Publication year - 2012
Publication title -
international journal of computer science and informatics
Language(s) - English
Resource type - Journals
ISSN - 2231-5292
DOI - 10.47893/ijcsi.2012.1074
Subject(s) - artificial intelligence , computer vision , computer science , feature (linguistics) , tracking (education) , kernel (algebra) , pattern recognition (psychology) , robustness (evolution) , tracking system , active appearance model , rotation (mathematics) , mathematics , image (mathematics) , kalman filter , psychology , pedagogy , philosophy , linguistics , combinatorics , biochemistry , chemistry , gene
An effective target modeling is the root of a robust and efficient tracking system. Color feature is widely used feature space for target modeling in real time tracking applications because of its computational efficiency and invariance towards change in shape, scale and rotation. The effective use of this feature with kernel-based target tracking can lead to a robust tracking system. This paper provides a comparative analysis of the performance of three variants of kernel-based tracking system using color feature. The simulation results show that the target modeling using transformed background weighted target model will perform efficiently when initialized target has similar color feature with background while the combination of color-texture will be more accurate and robust when texture features are prominently present.