A simulator-based approach to evaluating optical trackers

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 an ARToolkitPlus-based tracker under a fixed set of conditions. We also show how the framework can be used to assess the quality of various camera placements 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, and study the influence of random noise on tracker accuracy. 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.

[1]  Dieter Schmalstieg,et al.  ARToolKitPlus for Pose Trackin on Mobile Devices , 2007 .

[2]  Berthold K. P. Horn,et al.  Closed-form solution of absolute orientation using unit quaternions , 1987 .

[3]  A. J. vanRhijn Configurable Input Devices for 3D Interaction using Optical Tracking , 2007 .

[4]  Hirokazu Kato,et al.  Marker tracking and HMD calibration for a video-based augmented reality conferencing system , 1999, Proceedings 2nd IEEE and ACM International Workshop on Augmented Reality (IWAR'99).

[5]  Robert van Liere,et al.  GraphTracker: a topology projection invariant optical tracker , 2006, EGVE'06.

[6]  Terrance E. Boult,et al.  A New Closed Form Approach to the Absolute Orientation Problem , 1999 .

[7]  James Davis,et al.  Camera Placement Considering Occlusion for Robust Motion Capture , 2000 .

[8]  Vincent Lepetit,et al.  Accurate Non-Iterative O(n) Solution to the PnP Problem , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[9]  Greg Welch,et al.  An interactive camera placement and visibility simulator for image-based VR applications , 2006, Electronic Imaging.

[10]  Axel Pinz,et al.  Robust Pose Estimation from a Planar Target , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Robert van Liere,et al.  GraphTracker: a topology projection invariant optical tracker , 2006, EGVE'06.

[12]  Gregory D. Hager,et al.  Fast and Globally Convergent Pose Estimation from Video Images , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Wen-Yan Chang,et al.  Pose estimation for multiple camera systems , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[14]  Robert van Liere,et al.  An Experimental Comparison of Three Optical Trackers for Model Based Pose Determination in Virtual Reality , 2004, EGVE.

[15]  J. C. Mulder,et al.  The personal space station: Bringing interaction within reach , 2002 .