A technique for real time object tracking in a mobile computing environment and its application to video see-through Augmented Reality (AR) has been designed, verified through simulation, and implemented and validated on a mobile computing device. Using position based visual position and orientation (POSE) methods and the Extended Kalman Filter (EKF), it is shown how this technique lends itself to be flexible to tracking multiple objects and multiple object models using a single monocular camera on different mobile computing devices. Using the monocular camera of the mobile computing device, feature points of the object(s) are located through image processing on the display. The relative position and orientation between the device and the object(s) is determined recursively by an EKF process. Once the relative position and orientation is determined for each object, three dimensional AR image(s) are rendered onto the display as if the device is looking at the virtual object(s) in the real world. This application and the framework presented could be used in the future to overlay additional informational onto displays in mobile computing devices. Example applications include robotic aided surgery where animations could be overlaid to assist the surgeon, in training applications that could aid in operation of equipment or in search and rescue operations where critical information such as floor plans and directions could be virtually placed onto the display.
Current approaches in the field of real time object tracking are discussed along with the methods used for video see-through AR applications on mobile computing devices. The mathematical framework for the real time object tracking and video see-through AR rendering is discussed in detail along with some consideration to extension to the handling of multiple AR objects. A physical implementation for a mobile computing device is proposed detailing the algorithmic approach along with design decisions.
The real time object tracking and video see-through AR system proposed is verified through simulation and details around the accuracy, robustness, constraints, and an extension to multiple object tracking are presented. The system is then validated using a ground truth measurement system and the accuracy, robustness, and its limitations are reviewed. A detailed validation analysis is also presented showing the feasibility of extending this approach to multiple objects. Finally conclusions from this research are presented based on the findings of this work and further areas of study are proposed.
| Identifer | oai:union.ndltd.org:WATERLOO/oai:uwspace.uwaterloo.ca:10012/7754 |
| Date | 22 August 2013 |
| Creators | Fischer, Daniel |
| Source Sets | University of Waterloo Electronic Theses Repository |
| Language | English |
| Detected Language | English |
| Type | Thesis or Dissertation |
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