Applications for real-time visual tracking can be found in many areas, including visual odometry and augmented reality. Interest point detection and feature description form the basis of feature-based tracking, and a variety of algorithms for these tasks have been proposed. In this work, we present (1) a carefully designed dataset of video sequences of planar textures with ground truth, which includes various geometric changes, lighting conditions, and levels of motion blur, and which may serve as a testbed for a variety of tracking-related problems, and (2) a comprehensive quantitative evaluation of detector-descriptor-based visual camera tracking based on this testbed. We evaluate the impact of individual algorithm parameters, compare algorithms for both detection and description in isolation, as well as all detector-descriptor combinations as a tracking solution. In contrast to existing evaluations, which aim at different tasks such as object recognition and have limited validity for visual tracking, our evaluation is geared towards this application in all relevant factors (performance measures, testbed, candidate algorithms). To our knowledge, this is the first work that comprehensively compares these algorithms in this context, and in particular, on video streams.

Figure 1. Live AR-based remote collaboration with our prototype. Left: the local user in front of a car engine, identifying a particular element which the remote user has marked with the yellow dot. Right: the remote user's view onto the scene, which (at this moment) shows more context than the local user's current view. The latter is shown as an inset on the bottom left as well as being projected onto the model. The remote user can browse this environment independently of the local user's camera and can set annotations, which are immediately visible to the local user in AR. ABSTRACTWe present a system that supports an augmented shared visual space for live mobile remote collaboration on physical tasks. The remote user can explore the scene independently of the local user's current camera position and can communicate via spatial annotations that are immediately visible to the local user in augmented reality. Our system operates on off-the-shelf hardware and uses real-time visual tracking and modeling, thus not requiring any preparation or instrumentation of the environment. It creates a synergy between video conferencing and remote scene exploration under a unique coherent interface. To evaluate the collaboration with our system, we conducted an extensive outdoor user study with 60 participants comparing our system with two baseline interfaces. Our results indicate an overwhelming user preference (80%) for our system, a high level of usability, as well as performance benefits compared with one of the two baselines.

We describe a framework and prototype implementation for unobtrusive mobile remote collaboration on tasks that involve the physical environment. Our system uses the Augmented Reality paradigm and model-free, markerless visual tracking to facilitate decoupled, live updated views of the environment and world-stabilized annotations while supporting a moving camera and unknown, unprepared environments. In order to evaluate our concept and prototype, we conducted a user study with 48 participants in which a remote expert instructed a local user to operate a mock-up airplane cockpit. Users performed significantly better with our prototype (40.8 tasks completed on average) as well as with static annotations (37.3) than without annotations (28.9). 79% of the users preferred our prototype despite noticeably imperfect tracking.

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We describe an algorithm dubbed Suppression via Disk Covering (SDC) to efficiently select a set of strong, spatially distributed keypoints, and we show that selecting keypoint in this way significantly improves visual tracking. We also describe two efficient implementation schemes for the popular Adaptive Non-Maximal Suppression algorithm, and show empirically that SDC is significantly faster while providing the same improvements with respect to tracking robustness. In our particular application, using SDC to filter the output of an inexpensive (but, by itself, less reliable) keypoint detector (FAST) results in higher tracking robustness at significantly lower total cost than using a computationally more expensive detector.