Fast and Globally Convergent Pose Estimation from Video Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Marker Tracking and HMD Calibration for a Video-Based Augmented Reality Conferencing System
IWAR '99 Proceedings of the 2nd IEEE and ACM International Workshop on Augmented Reality
Pose Estimation for Multiple Camera Systems
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Robust Pose Estimation from a Planar Target
IEEE Transactions on Pattern Analysis and Machine Intelligence
Virtual Environments: Graphtracker: A topology projection invariant optical tracker
Computers and Graphics
EGVE'04 Proceedings of the Tenth Eurographics conference on Virtual Environments
Methodology for evaluating static six-degree-of-freedom (6DoF) perception systems
Proceedings of the 10th Performance Metrics for Intelligent Systems Workshop
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We describe a software framework to evaluate the performance of model-based optical trackers in virtual environments. The framework can be used to evaluate and compare the performance of different trackers under various conditions, to study the effects of varying intrinsic and extrinsic camera properties, and to study the effects of environmental conditions on tracker performance. The framework consists of a simulator that, given various input conditions, generates a series of images. The input conditions of the framework model important aspects, such as the interaction task, input device geometry, camera properties and occlusion. As a concrete case, we illustrate the usage of the proposed framework for input device tracking in a near-field desktop virtual environment. We compare the performance of an in-house tracker with the ARToolkit tracker under a fixed set of conditions. We also show how the framework can be used to find the optimal camera parameters given a pre-recorded interaction task. Finally, we use the framework to determine the minimum required camera resolution for a desktop, Workbench and CAVE environment. The framework is shown to provide an efficient and simple method to study various conditions affecting optical tracker performance. Furthermore, it can be used as a valuable development tool to aid in the construction of optical trackers.