Abstract:
Unmanned Aerial Vehicles (UAVs) are now common place and their sensor solutions are
producing ever increasing volumes of data. Typically the data is based around the theme
of remote sensing of the Earth, and is gathered by a multitude of sensors for differing
applications. The requirement to process the data gathered into useful information grows
as does the demand for intelligent systems to assist with this. The most common, cost
effective and readily available sensor solution is through standard camera photography,
and offers the most usable data format without specialist tools. This also allows for proven
methods to process the data gathered by a UAV thorough image processing and
computation vision. One consistent theme in computer vision research is the drive for the
ability to accurately reconstruct 3D scenes from 2D imagery through the process of
Structure from Motion (SfM). This thesis details the research into the use of this 3D
imagery, specifically aiding the ability to detect temporal change in dynamic scenes. This
work presents a new technique to increase probability of detection and reduce
computation required for such a process, the 3D Structure and Colour (3DSAC)
differencing technique. The technique also goes to present a visualisation ability that best
uses the algorithm for additional end user analysis beyond that of mathematics. Three
scenarios where complex non-uniform changes are presented, of which assess and
validate this technique to offer a capability to cope with dynamic scenes. The weighted
3DSAC algorithm gives the end user the ability to configure with emphasis being placed
more within either structural or colour changes. Finally, through the implementation and
evaluation of other current state of the art techniques for describing 3D points, the
research shows the 3DSAC technique is more performant with imagery gathered by low
altitude UAVs.
