dc.contributor.author | Babu, Vignesh | |
dc.date.accessioned | 2015-08-06T17:16:19Z | |
dc.date.available | 2015-08-06T17:16:19Z | |
dc.date.issued | 2015 | |
dc.identifier.uri | http://hdl.handle.net/10222/59144 | |
dc.description.abstract | Unmanned aerial vehicles commercially called quadcopters or drones have become
increasingly popular over recent years, delving into wide range of fields from medicine
for providing immediate health care or in agriculture for locating damaged crops using
special sensors to being used in quarries for 3d mapping.
We focus on the application of drones in adaptive long term tracking of an object-of-interest
and following it with necessary collision avoidance. For this we have implemented a tracking
framework called TLD, employing an integrated stereo camera on-board the commercial
drone Spiri as the sensor to perform long-term tracking of a target object and use the depth
map generated from the disparity of the stereo camera to maintain necessary distance from
the target. This is built over the ROS framework. We examine and demonstrate this design
in real-time on a commercial drone with monocular camera and in simulation on a model
drone integrated with stereo camera. We further refined the tracking process by remodeling
TLDs tracker to work with SIFT features supplemented by depth information.
We present the evaluation results to show the improvements achieved by our algorithm to
autonomously maneuver the drone in making smooth and rapid transitions and then provide
comparisons to show improved tracking resilience against modest change in object appearance
immediately following system initialization. | en_US |
dc.language.iso | en | en_US |
dc.subject | TLD | en_US |
dc.subject | autonomous | en_US |
dc.subject | UAV | en_US |
dc.subject | stereo camera | en_US |
dc.subject | adaptive | en_US |
dc.subject | tracking | en_US |
dc.subject | drone | en_US |
dc.subject | tracking-following | en_US |
dc.subject | UGV | en_US |
dc.subject | collision avoidance | en_US |
dc.subject | machine learning | en_US |
dc.subject | robotics | en_US |
dc.title | ADAPTIVE LONG TERM TRACKING AND AUTONOMOUS FOLLOWING USING STEREO-CAMERA OF AN UNMANNED AERIAL VEHICLE WITH COLLISION AVOIDANCE | en_US |
dc.date.defence | 2015-07-20 | |
dc.contributor.department | Faculty of Computer Science | en_US |
dc.contributor.degree | Master of Computer Science | en_US |
dc.contributor.external-examiner | n/a | en_US |
dc.contributor.graduate-coordinator | Dr. Evangelos Milios | en_US |
dc.contributor.thesis-reader | Dr. Mae Seto | en_US |
dc.contributor.thesis-reader | Dr. Evangelos Milios | en_US |
dc.contributor.thesis-supervisor | Dr. Thomas Trappenberg | en_US |
dc.contributor.ethics-approval | Not Applicable | en_US |
dc.contributor.manuscripts | Not Applicable | en_US |
dc.contributor.copyright-release | Not Applicable | en_US |