The focus of this workshop is on all issues related to tracking for mixed and augmented reality applications. Unlike the tracking sessions of the main conference, this workshop does not require pure novelty of the proposed methods; it rather encourages presentations that concentrate on complete systems and integrated approaches engineered to run in real-world scenarios. The research felds covered include self-localization using computer vision or other sensing modalities (such as depth cameras, GPS, inertial, etc.) and tracking systems issues (such as system design, calibration, estimation, fusion, etc.). This year's focus is also expanded to research on object detection and semantic scene understanding with relevance to augmented reality. Implementations on mobile devices and under real-time constraints are also part of the workshop focus. These are issues of core importance for practical augmented reality systems.

In this proposal we show case two 3D reconstruction systems running in real-time on a tablet equipped with a depth sensor. We believe that the proposed set of demonstrations will engage ISMAR attendees both in terms of tracking technology and user experience. Both demos show state-of-the art 3D reconstruction technology and give attendees a chance to try hands-on our tracking with simple and interactive user interfaces.

In this proposal we suggest a set of demonstrations to be presented at ISMAR 2014 around the topic of monocular tracking, mapping and rendering on mobile phones. We believe that the proposed set of demonstrations will engage ISMAR attendees both in terms of tracking technology and user experience. On the one hand, all demos show state-of-the art tracking and mapping technology and gives attendees a chance to try hands-on our tracking solutions and to discuss them with our researchers and developers. All demos also showcase compelling use cases enabled by our technology, encouraging discussions on topics such as integration of tracking technology with game engines and user experience research.

The development of new Simultaneous Localization and Mapping (SLAM) techniques is quickly advancing in research communities and rapidly transitioning into commercial products. Creating accurate and high-quality SLAM maps relies on a robust initialization process. However, the robustness and usability of SLAM initialization for end-users has often been disregarded. This paper presents and evaluates a novel tracking system for 6DOF pose tracking between a single keyframe and the current camera frame, without any prior scene knowledge. Our system is particularly suitable for SLAM initialization, since it allows 6DOF pose tracking in the intermediate frames before a wide-enough baseline between two keyframes has formed. We investigate how our tracking system can be used to interactively guide users in performing an optimal motion for SLAM initialization. However, our findings from a pilot study indicate that the need for such motion can be completely hidden from the user and outsourced to our tracking system. Results from a second user study show that letting our tracking system create a SLAM map as soon as possible is a viable and usable solution. Our work provides important insight for SLAM systems, showing how our novel tracking system can be integrated with a user interface to support fast, robust and user-friendly SLAM initialization.

The focus of this workshop is on presenting, discussing and demonstrating recent tracking methods and applications that work well in practice and that show some superiority over state-of-the-art methods. Rather than focusing on pure novelty, this workshop encourages presentations that concentrate on complete systems and integrated approaches. The TMA workshop looks at pose tracking from an end-to-end point of view.

Academic/Research Overview of Mobile Augmented Reality Introduction to the Qualcomm AR Platform — Features and Usage Scenarios Developing Mobile AR Applications using Qualcomm's Unity Extension Introduction to Qualcomm's QCAR SDK Native API Cross Platform Development with the Native QCAR SDK

This paper investigates possible physical alterations of tracking targets to obtain improved 6DoF pose detection for a camera observing the known targets. We explore the influence of several texture characteristics on the pose detection, by simulating a large number of different target objects and camera poses. Based on statistical observations, we rank the importance of characteristics such as texturedness and feature distribution for a specific implementation of a 6DoF tracking technique. These findings allow informed modification strategies for improving the tracking target objects themselves, in the common case of man-made targets, as for example used in advertising. This fundamentally differs from and complements the traditional approach of leaving the targets unchanged while trying to optimize the tracking algorithms and parameters.

This paper presents a multi-pronged study of users' location-sharing practices in the context of online social networks. The contribution of this study is two-fold: first it presents a series of insights relating to location-sharing practices, and second it highlights the use of third-person scenarios as a useful method for eliciting privacy concerns and potentially educating users.

We present a novel method for the real-time creation and tracking of panoramic maps on mobile phones. The maps generated with this technique are visually appealing, very accurate and allow drift-free rotation tracking. This method runs on mobile phones at 30 Hz and has applications in the creation of panoramic images for offline browsing, for visual enhancements through environment mapping and for outdoor Augmented Reality on mobile phones.

In this paper, we present three techniques for 6DOF natural feature tracking in real time on mobile phones. We achieve interactive frame rates of up to 30 Hz for natural feature tracking from textured planar targets on current generation phones. We use an approach based on heavily modified state-of-the-art feature descriptors, namely SIFT and Ferns plus a template-matching-based tracker. While SIFT is known to be a strong, but computationally expensive feature descriptor, Ferns classification is fast, but requires large amounts of memory. This renders both original designs unsuitable for mobile phones. We give detailed descriptions on how we modified both approaches to make them suitable for mobile phones. The template-based tracker further increases the performance and robustness of the SIFT- and Ferns-based approaches. We present evaluations on robustness and performance and discuss their appropriateness for Augmented Reality applications.

We present a fast and memory efficient method for localizing a mobile user's 6DOF pose from a single camera image. Our approach registers a view with respect to a sparse 3D point reconstruction. The 3D point dataset is partitioned into pieces based on visibility constraints and occlusion culling, making it scalable and efficient to handle. Starting with a coarse guess, our system only considers features that can be seen from the user's position. Our method is resource efficient, usually requiring only a few megabytes of memory, thereby making it feasible to run on low-end devices such as mobile phones. At the same time it is fast enough to give instant results on this device class.

In this work we present a novel idea of evaluating natural feature-point based tracking targets. Our main objective is to evaluate the inherent characteristics of natural feature-point sets with respect to vision-based pose estimation algorithms. Our work attempts to break new ground by concentrating on evaluating complete tracking targets, rather than evaluating tracking methods or single features. This allows deriving indications on how to improve the trackability of natural feature point sets.

Outdoor Augmented Reality typically requires tracking in unprepared environments. For global registration, Global Positioning System (GPS) is currently the best sensing technology, but its precision and update rate are not sufficient for high quality tracking. We present a system that uses Kalman filtering for fusion of Differential GPS (DGPS) or Real-Time Kinematic (RTK) based GPS with barometric heights and also for an inertial measurement unit with gyroscopes, magnetometers and accelerometers to improve the transient oscillation. Typically, inertial sensors are subjected to drift and magnetometer measurements are distorted by electro-magnetic fields in the environment. For compensation, we additionally apply a visual orientation tracker which is drift-free through online mapping of the unknown environment. This tracker allows for correction of distortions of the 3-axis magnetic compass, which increases the robustness and accuracy of the pose estimates. We present results of applying this approach in an industrial application scenario.

In this paper we present a novel method for real-time pose estimation and tracking on low-end devices such as mobile phones. The presented system can track multiple known targets in real-time and simultaneously detect new targets for tracking. We present a method to automatically and dynamically balance the quality of detection and tracking to adapt to a variable time budget and ensure a constant frame rate. Results from real data of a mobile phone Augmented Reality system demonstrate the efficiency and robustness of the described approach. The system can track 6 planar targets on a mobile phone simultaneously at framerates of 23 fps.

Marker tracking has revolutionized augmented reality about a decade ago. However, this revolution came at the expense of visual clutter. In this paper, we propose several new marker techniques, which are less obtrusive than the usual black and white squares. Furthermore, we report methods that allow tracking beyond the visibility of these markers further improving robustness. All presented techniques are implemented in a single tracking library, are highly efficient in their memory and CPU usage and run at interactive frame rates on mobile phones.

In this paper we present two techniques for natural feature tracking in real-time on mobile phones. We achieve interactive frame rates of up to 20 Hz for natural feature tracking from textured planar targets on current-generation phones. We use an approach based on heavily modified state-of-the-art feature descriptors, namely SIFT and Ferns. While SIFT is known to be a strong, but computationally expensive feature descriptor, Ferns classification is fast, but requires large amounts of memory. This renders both original designs unsuitable for mobile phones. We give detailed descriptions on how we modified both approaches to make them suitable for mobile phones. We present evaluations on robustness and performance on various devices and finally discuss their appropriateness for augmented reality applications.

In this paper, we present Studierstube ES, a framework for the development of handheld Augmented Reality. The applications run self-contained on handheld computers and smartphones with Windows CE. A detailed description of the performance critical tracking and rendering components are given. We also report on the implementation of a client-server architecture for multi-user applications, and a game engine for location based museum games that has been built on top of this infrastructure. Details on two games that were created, permanently deployed and evaluated in two Austrian museums illustrate the practical value of the framework and lessons learned from using it.

Augmented Reality (AR) can naturally complement mobile computing on wearable devices by providing an intuitive interface to a three-dimensional information space embedded within physical reality. However, existing AR systems like MARS [1] or Tinmith [2], which require a user to wear a notebook computer in a backpack and a head-mounted display (HMD) are expensive, fragile and inconvenient to wear. Thin-client approaches using a Tablet PC or Personal Digital Assistant (PDA) merely as a portable display [3][4] require a dedicated server infrastructure and limit mobility. We believe there is a need for an unconstrained, infrastructureindependent AR display running to fill the gap in situations where traditional backpack systems are too costly and cumbersome, but thin client implementations exhibit inadequate deployability, scalability or interactive behavior. Particular examples include sporadic use over lengthy time spans, in between which devices must be stowed away, mixed indoor/outdoor use in wide-area environments, and massively multi-user application scenarios. This has motivated us to develop a state of the art AR framework targeting lightweight handheld displays.

No abstract available.

ARToolKit programmers are familiar with the kanji symbols supplied with the distribution. Most of them do not know what these kanji symbols mean. We propose a piece of educational software that uses collaborative augmented reality (AR) to teach users the meaning of kanji symbols. The application is laid out as a two player augmented reality computer game. The novelty of our approach is that we do not use regular workstations or laptops to host the AR (augmented reality) application. Instead we use fully autonomous PDAs, running the application together with an optical marker-based tracking module that makes this application not only available for a broad audience but also optimally mobile.