Vehicle tracking under occlusion conditions using directional ringlet intensity feature transform

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 pro...

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Bibliographic Details
Main Authors: Krieger, Evan, Sidike, Paheding, Aspiras, Theus, Asari, Vijayan K.
Format: Conference Proceeding
Language:English
Subjects:
Aerospace
Avionics
Drift
Feature extraction
Gaussian ringlet
Histograms
image enhancement
Kalman filters
Kernel
Kirsch mask
Object tracking
Occlusion
occlusion handling
Search problems
Tracking
Transforms
Vehicles
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Description
Summary: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.
ISSN:2379-2027
DOI:10.1109/NAECON.2015.7443041