Document Type
Conference Paper
Publication Date
9-2015
Publication Source
2015 IEEE Conference on Image Processing
Abstract
The challenges existing for current intensity-based histogram feature tracking methods in wide area motion imagery include object structural information distortions and background variations, such as different pavement or ground types. All of these challenges need to be met in order to have a robust object tracker, while attaining to be computed at an appropriate speed for real-time processing. To achieve this we propose a novel method, Directional Ringlet Intensity Feature Transform (DRIFT), that employs Kirsch kernel filtering and Gaussian ringlet feature mapping. We evaluated the DRIFT on two challenging datasets, namely Columbus Large Image Format (CLIF) and Large Area Image Recorder (LAIR), to evaluate its robustness and efficiency. Experimental results show that the proposed approach yields the highest accuracy compared to state-of-the-art object tracking methods.
Inclusive pages
1-5
ISBN/ISSN
978-1-4799-8338-4
Document Version
Postprint
Copyright
Copyright © 2015, IEEE
Publisher
IEEE
Place of Publication
Quebec City, Canada
eCommons Citation
Krieger, Evan; Sidike, Paheding; Aspiras, Theus H.; and Asari, Vijayan K., "Directional Ringlet Intensity Feature Transform for Tracking" (2015). Electrical and Computer Engineering Faculty Publications. 375.
https://ecommons.udayton.edu/ece_fac_pub/375
Comments
The document available for download is the authors' accepted manuscript, provided in compliance with the publisher's policy on self-archiving. Permission documentation is on file. The version of record is available using the DOI provided.