Object Tracking by Normalized Cross Correlation and PCA Based Template Updating: Comparative Analysis | Zendy

M. H. Sidram | Zendy; Nagappa U. Bhajantri | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Object Tracking by Normalized Cross Correlation and PCA Based Template Updating: Comparative Analysis

Author(s) -

M. H. Sidram,

Nagappa U. Bhajantri

Publication year - 2014

Publication title -

international journal of computer science and informatics

Language(s) - English

Resource type - Journals

ISSN - 2231-5292

DOI - 10.47893/ijcsi.2014.1143

Subject(s) - principal component analysis , artificial intelligence , computer science , template matching , kernel (algebra) , kernel principal component analysis , object (grammar) , pattern recognition (psychology) , track (disk drive) , matching (statistics) , cross correlation , computer vision , tracking (education) , sequence (biology) , mathematics , kernel method , image (mathematics) , statistics , support vector machine , psychology , pedagogy , combinatorics , biology , genetics , operating system

The principle behind to detect and track non-stationary object via a sequence of frames is addressed. The proposed strategy pushed the Normalized Cross-Correlation (NCCR) to track object by matching the template and updating the template is encouraged through Principal Component Analysis (PCA). This work remarked with exhaustive experiment and witnessed with comparative analysis over dataset related to outdoor environment. The system kernel reveals the capability to track the object and outcome is fairly acceptable to great extent under different light conditions.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research