Vehicle Tracking under Occlusion Conditions using Directional Ringlet Intensity Feature Transform

Author(s)

Evan Krieger, University of Dayton
Paheding Sidike, University of Dayton
Theus H. Aspiras, University of Dayton
Vijayan K. Asari, University of DaytonFollow

Document Type

Conference Paper

Publication Date

6-2015

Publication Source

2015 National Aerospace and Electronics Conference

Abstract

The tracking of vehicles in wide area motion imagery (WAMI) can be a challenge due to the full and partial occlusions that can occur. The proposed solution for this challenge is to use the Directional Ringlet Intensity Feature Transform (DRIFT) feature extraction method with a Kalman filter. The proposed solution will utilize the properties of the DRIFT feature to solve the partial occlusion challenges. The Kalman filter will be used to estimate the object location during a full occlusion. The proposed solution will be tested on several vehicle sequences from the Columbus Large Image Format (CLIF) dataset.

ISBN/ISSN

2379-2027

Comments

Permission documentation is on file.

Copyright

Copyright © 2015, IEEE

Publisher

IEEE

Place of Publication

Dayton, OH

eCommons Citation

Krieger, Evan; Sidike, Paheding; Aspiras, Theus H.; and Asari, Vijayan K., "Vehicle Tracking under Occlusion Conditions using Directional Ringlet Intensity Feature Transform" (2015). Electrical and Computer Engineering Faculty Publications. 371.
https://ecommons.udayton.edu/ece_fac_pub/371