Volger, M. (2015) Human detection and recognition in visual data from a swarm of unmanned aerial and ground vehicles through dynamic navigation. Master's Thesis / Essay, Artificial Intelligence.
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Abstract
One of the hot topics in current Artificial Intelligence research and in society are outdoor unmanned systems and their recent applications. Development in sensor output processing and computer vision is one of the main reasons for the rapid growth in the abilities of such systems to operate autonomously. Detecting and recognizing objects and humans has been a prominent subject in research since computer vision originated. Combining the field of outdoor unmanned systems with computer vision yields interesting new research topics. Reactive vehicle behaviors and possible human recognition opposed to solely detections from such systems is a fairly unexplored side of the scientific field. The current research focuses on autonomous human detection and recognition in real-time sensory data from unmanned ground vehicles (UGV) and unmanned aerial vehicles (UAV) through dynamic navigation. Additional information and heightened perception can be gained by creating intelligent navigational behaviors combined with well performing object classifiers. More specifically, the autonomous vehicles in the architecture search for test subjects in a field and react upon those detections. If a person is detected in the camera imagery, a vehicle will dynamically stray off its initial search pattern to gain more information on the subject. The dynamic navigation is used to approach the subject and to attempt facial recognition using a data set of the test subjects. Through the deployment of a heterogeneous swarm of multiple UGVs and UAVs individual search spaces can be decreased and detection rates increased. The research was built upon a software architecture called CongreGators that controls a swarm of autonomous vehicles. A complete system for the autonomous detection and recognition of human subjects through dynamic navigation with a heterogeneous swarm of autonomous agents was implemented and tested. Dynamic navigation patterns were created and optimized to increase the perception and information gain of the robotic systems at hand. The CongreGators architecture was created at the University of Florida's Machine Intelligence Laboratory, where the current research was conducted as well.
| Item Type: | Thesis (Master's Thesis / Essay) |
|---|---|
| Degree programme: | Artificial Intelligence |
| Thesis type: | Master's Thesis / Essay |
| Language: | English |
| Date Deposited: | 15 Feb 2018 08:02 |
| Last Modified: | 15 Feb 2018 08:02 |
| URI: | https://fse.studenttheses.ub.rug.nl/id/eprint/12434 |
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