Abstract

Video from aerial surveillance can provide a rich source of data for analysts. From the time-critical perspective of wilderness search and rescue operations, information extracted from aerial videos can mean the difference between a successful search and an unsuccessful search. When using low-cost, payload-limited mini-UAVs, as opposed to more expensive platforms, several challenges arise, including jittery video, narrow fields of view, low resolution, and limited time on screen for key features. These challenges make it difficult for analysts to extract key information in a timely manner. Traditional approaches may address some of these issues, but no existing system effectively addresses all of them in a unified and efficient manner. Building upon a hierarchical dense image correspondence technique, we create a unifying framework for reducing jitter, enhancing resolution, and expanding the field of view while lengthening the time that features remain on screen. It also provides for easy extraction of moving objects in the scene. Our method incorporates locally adaptive warps which allows for robust image alignment even in the presence of parallax and without the aid of internal or external camera parameters. We accelerate the image registration process using commodity Graphics Processing Units (GPUs) to accomplish all of these tasks in near real-time with no external telemetry data.

Degree

College and Department

Physical and Mathematical Sciences; Computer Science

Rights

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