A tracker selection model for augmented reality applications

Performance degradation When running augmented reality applications in wearable platforms can be greater than expected, as desktop and mobile platforms present different levels of hardware capabilities, and consequently, different performance restrictions. This work addresses the object tracking problem using a decision model that prioritizes using the least computationally intensive algorithm whenever possible. This could be achieved by analyzing different metrics regarding image interference such as occlusion and image noise. The OTS (Object Tracking SWitcher) model allows automatic switching of different trackers to balance application's performance without compromising its tracking quality. It was validated by synthetic case studies that comprise different object classes that can be tracked, focusing on augmented reality applications that can run on restricted platforms. It was possible to reach a performance improvement of a factor of three, While keeping a minimum quality defined by a reprojection error of 10 pixels when compared to the use of only the best algorithm independent of its computational cost.