Directional ringlet intensity feature transform for tracking in enhanced wide area motion imagery

Date of Award


Degree Name

M.S. in Electrical Engineering


Department of Electrical and Computer Engineering


Advisor: Vijayan K. Asari


Object tracking in the wide area motion imagery (WAMI) data may be subjected to many challenges including object occlusion, rotation, scaling, illumination changes, and background variations. In addition, the target objects for tracking in WAMI data are typically low resolution and captured in complex lighting conditions. A novel feature extraction method along with a preprocessing stage is proposed in this thesis research to improve the accuracy of object tracking in these challenging environments. The local preprocessing algorithm performs illumination and spatial enhancement. The illumination enhancement algorithm utilizes a self-tunable transformation function (STTF), which is a nonlinear inverse sine transform, along with an improved color restoration and a halo reduction technique. The spatial enhancement algorithm is a single image super resolution technique that uses Fourier phase features and an adaptive kernel regression technique on the intensity channel. An intelligent methodology for integrating the intensity and spatial enhancement algorithms is developed to improve the tracking performance in the WAMI data. A robust feature-based tracking solution based on the Gaussian ringlet intensity distribution (GRID) feature extraction method is proposed in this thesis. GRID uses Gaussian ring histograms to create features that are robust to object rotation, illumination, and partial object occlusion. However, certain conditions, such as background variations and object structural information distortions, continue to cause feature mismatching. Hence, a new ringlet masking strategy that utilizes the rotational invariance of the Gaussian ringlet and directional edge information of the Kirsch kernel is proposed as the feature descriptor. The new Directional Ringlet Intensity Feature Transform (DRIFT) descriptor weighs the intensity and edge information of the reference object with the ringlet features to achieve robustness to object distortions and background variations. When utilized with a tracking technique with occlusion handling, the DRIFT feature will allow for more accurate tracking of the target object in complex lighting and background conditions. The performance of the proposed feature extraction technique is evaluated on a variety of WAMI object sequences from the Columbus Large Image Format (CLIF) dataset to determine their impact on the tracking process. The results from these evaluations show that the proposed DRIFT algorithm improves the accuracy of tracking on WAMI data and surpasses the performance of other state-of-the-art tracking methods. Future works in this direction include creating a method to handle scale change and automatic selection of super resolution factors based on the target object properties.


Photography Masking, Image processing Digital techniques, Video surveillance, Electrical Engineering, object tracking, Kirsch mask, Gaussian ringlet, image enhancement, super-resolution, feature extraction

Rights Statement

Copyright 2015, author