Videos recorded with current mobile devices are increasingly geotagged at fine granularity and used in various location based applications and services. However, raw sensor data collected is often noisy, resulting in subsequent inaccurate geospatial analysis. In this study, we focus on the challenging correction of compass readings and present an automatic approach to reduce these metadata errors. Given the small geo-distance between consecutive video frames, image-based localization does not work due to the high ambiguity in the depth reconstruction of the scene. As an alternative, we collect geographic context from OpenStreetMap and estimate the absolute viewing direction by comparing the image scene to world projections obtained with different external camera parameters. To design a comprehensive model, we further incorporate smooth approximation and feature-based rotation estimation when formulating the error terms. Experimental results show that our proposed pyramid-based method outperforms its competitors and reduces orientation errors by an average of 58.8%. Hence, for downstream applications, improved results can be obtained with these more accurate geo-metadata. To illustrate, we present the performance gain in landmark retrieval and tag suggestion by utilizing the accuracy-enhanced geo-metadata.

Accurate map matching has been a fundamental but challenging problem that has drawn great research attention in recent years. It aims to reduce the uncertainty in a trajectory by matching the GPS points to the road network on a digital map. Most existing work has focused on estimating the likelihood of a candidate path based on the GPS observations, while neglecting to model the probability of a route choice from the perspective of drivers. Here we propose a novel feature-based map matching algorithm that estimates the cost of a candidate path based on both GPS observations and human factors. To take human factors into consideration is very important especially when dealing with low sampling rate data where most of the movement details are lost. Additionally, we simultaneously analyze a subsequence of coherent GPS points by utilizing a new segment-based probabilistic map matching strategy, which is less susceptible to the noisiness of the positioning data. We have evaluated the proposed approach on a public large-scale GPS dataset, which consists of 100 trajectories distributed all over the world. The experimental results show that our method is robust to sparse data with large sampling intervals (e.g., 60 s to 300 s) and challenging track features (e.g., u-turns and loops). Compared with two state-of-the-art map matching algorithms, our method substantially reduces the route mismatch error by 6.4% to 32.3% and obtains the best map matching results in all the different combinations of sampling rates and challenging features.

Improvements in sensor and wireless network enable accurate, automated, instant determination and dissemination of a user's or objects position. The new enabler of location-based services (LBSs) apart from the current ubiquitous networking infrastructure is the enrichment of the different systems with semantics information, such as time, location, individual capability, preference and more. Such semantically enriched system-modeling aims at developing applications with enhanced functionality and advanced reasoning capabilities. These systems are able to deliver more personalized services to users by domain knowledge with advanced reasoning mechanisms, and provide solutions to problems that were otherwise infeasible. This approach also takes user's preference and place property into consideration that can be utilized to achieve a comprehensive range of personalized services, such as advertising, recommendations, or polling. This paper provides an overview of indoor localization technologies, popular models for extracting semantics from location data, approaches for associating semantic information and location data, and applications that may be enabled with location semantics. To make the presentation easy to understand, we will use a museum scenario to explain pros and cons of different technologies and models. More specifically, we will first explore users' needs in a museum scenario. Based on these needs, we will then discuss advantages and disadvantages of using different localization technologies to meet these needs. From these discussions, we can highlight gaps between real application requirements and existing technologies, and point out promising localization research directions. By identifying gaps between various models and real application requirements, we can draw a road map for future location semantics research.

We propose a robust pointing detection with virtual shadow representation for interacting with a public display. Using a depth camera, our shadow is generated by a model with an angled virtual sun light and detects the nearest point as a pointer. Position of the shadow becomes higher when user walks closer, which conveys the notion of correct distance to control the pointer and offers accessibility to the higher area of the display.

The proliferation of workplace multimedia collaboration applications has meant on one hand more opportunities for group work but on the other more data locked away in proprietary interfaces. We are developing new tools to capture and access multimedia content from any source. In this demo, we focus primarily on new methods that allow users to rapidly reconstitute, enhance, and share document-based information.

Adapting to personal needs and supporting correct posture are important in physiotherapy training. In this demo, we show a dual screen application (handheld and TV) that allows patients to view hypervideo training programs. Designed to guide their daily exercises, these programs can be adapted to daily needs. The dual screen concept offers the positional flexibility missing in single screen solutions.

Dual screen concepts for hypervideo-based physiotherapy training are important in healthcare settings, but existing applications often cannot be adapted to personal needs and do not support correct posture. In this paper, we describe the design and implementation of a dual screen application (handheld and TV) that allows patients to view hypervideos designed to help them correctly perform their exercises. This approach lets patients adapt their training to their daily needs and their overall training progress. We evaluated this prototypical implementation in a user test with post-operative care prostate cancer patients. From our results, we derived design recommendations for dual screen physical training hypervideo applications.

Different systems exist for the creation of hypervideos nowadays. However, the creation of the video scenes which are put together to a hypervideo is a tedious and time consuming job. Then again huge video databases like YouTube exist which already provide rich sources of video materials. Yet it is not allowed to download and re-purpose the videos from there legally, which requires a solution to link whole videos or parts of videos and play them from the platform in an embedded player. This work presents the SIVA Web Producer, a Chrome extension for the creation of hypervideos consisting of scenes from YouTube videos. After creating a project, the Chrome extension allows to import YouTube videos or parts thereof as video clips. These can than be linked to a scene graph. A preview is provided and finalized videos can be published on the SIVA Web Portal.

In this paper we describe DocuGram, a novel tool to capture and share documents from any application. As users scroll through pages of their document inside the native application (Word, Google Docs, web browser), the system captures and analyses in real-time the video frames and reconstitutes the original document pages into an easy to view HTML-based representation. In addition to regenerating the document pages, a DocuGram also includes the interactions users had over them, e.g. mouse motions and voice comments. A DocuGram acts as a modern copy machine, allowing users to copy and share any document from any application.

Most teleconferencing tools treat users in distributed meetings monolithically: all participants are meant to be connected to one another in the more-or-less the same manner. In reality, though, people connect to meetings in all manner of different contexts, sometimes sitting in front of a laptop or tablet giving their full attention, but at other times mobile and involved in other tasks or as a liminal participant in a larger group meeting. In this paper we present the design and evaluation of two applications, Penny and MeetingMate, designed to help users in non-standard contexts participate in meetings.

The abundance of data posted to Twitter enables companies to extract useful information, such as Twitter users who are dissatisfied with a product. We endeavor to determine which Twitter users are potential customers for companies and would be receptive to product recommendations through the language they use in tweets after mentioning a product of interest. With Twitter's API, we collected tweets from users who tweeted about mobile devices or cameras. An expert annotator determined whether each tweet was relevant to customer purchase behavior and whether a user, based on their tweets, eventually bought the product. For the relevance task, among four models, a feed-forward neural network yielded the best cross-validation accuracy of over 80% per product. For customer purchase prediction of a product, we observed improved performance with the use of sequential input of tweets to recurrent models, with an LSTM model being best; we also observed the use of relevance predictions in our model to be more effective with less powerful RNNs and on more difficult tasks.

Two related challenges with current teleoperated robotic systems are lack of peripheral vision and awareness, and difficulty or tedium of navigating through remote spaces. We address these challenges by providing an interface with a focus plus context (F+C) view of the robot location, and where the user can navigate simply by looking where they want to go, and clicking or drawing a path on the view to indicate the desired trajectory or destination. The F+C view provides an undistorted, perspectively correct central region surrounded by a wide field of view peripheral portion, and avoids the need for separate views. The navigation method is direct and intuitive in comparison to keyboard or joystick based navigation, which require the user to be in a control loop as the robot moves. Both the F+C views and the direct click navigation were evaluated in a preliminary user study.

Mobile Telepresence Robots (MTR) are an emerging technology that extend the functionality of telepresence systems by adding mobility. MTRs nowadays, however, rely on stationary imaging systems such as a single narrow-view camera for vision, which can lead to reduced operator performance due to view-related deficiencies in situational awareness. We therefore developed an improved imaging and viewing platform that allows immersive telepresence using a Head Mounted Device (HMD) with head-tracked mono and stereoscopic video. Using a remote collaboration task to ground our research, we examine the effectiveness head-tracked HMD systems in comparison to a baseline monitor-based system. We performed a user study where participants were divided into three groups: fixed camera monitor-based baseline condition (without HMD), HMD with head-tracked 2D camera and HMD with head-tracked stereo camera. Results showed the use of HMD reduces task error rates and improves perceived collaborative success and quality of view, compared to the baseline condition. No major difference was found, however, between stereo and 2D camera conditions for participants wearing an HMD.

Social media offers potential opportunities for businesses to extract business intelligence. This paper presents Tweetviz, an interactive tool to help businesses extract actionable information from a large set of noisy Twitter messages. Tweetviz visualizes tweet sentiment of business locations, identifies other business venues that Twitter users visit, and estimates some simple demographics of the Twitter users frequenting a business. A user study to evaluate the system's ability indicates that Tweetviz can provide an overview of a business's issues and sentiment as well as information aiding users in creating customer profiles.

Web videos are becoming more and more popular. Current web technologies make it simpler than ever to both stream videos and create complex constructs of interlinked videos with additional information (video, audio, images, and text); so called hypervideos. When viewers interact with hypervideos by clicking on links, new content has to be loaded. This may lead to excessive waiting times, interrupting the presentation -- especially when videos are loaded into the hypervideo player. In this work, we propose hypervideo pre-fetching strategies, which can be implemented in players to minimize waiting times. We examine the possibilities offered by the HTML5

Mobile Audio Commander (MAC) is a mobile phone-based multimedia sensing system that facilitates the introduction of extra sensors to existing mobile robots for advanced capabilities. In this paper, we use MAC to introduce an accurate indoor positioning sensor to a robot to facilitate its indoor navigation. More specifically, we use a projector to send out position ID through light signal, use a light sensor and the audio channel on a mobile phone to decode the position ID, and send navigation commands to a target robot through audio output. With this setup, our system can simplify user’s robot navigation. Users can define a robot navigation path on a phone, and our system will compare the navigation path with its accurate location sensor inputs and generate analog line-following signal, collision avoidance signal, and analog angular signal to adjust the robot’s straight movements and turns. This paper describes two examples of using MAC and a positioning system to enable complicated robot navigation with proper user interface design, external circuit design and real sensor installations on existing robots.

Captions are a central component in image posts that communicate the background story behind photos. Captions can enhance the engagement with audiences and are therefore critical to campaigns or advertisement. Previous studies in image captioning either rely solely on image content or summarize multiple web documents related to image's location; both neglect users' activities. We propose business-aware latent topics as a new contextual cue for image captioning that represent user activities. The idea is to learn the typical activities of people who posted images from business venues with similar categories (e.g., fast food restaurants) to provide appropriate context for similar topics (e.g., burger) in new posts. User activities are modeled via a latent topic representation. In turn, the image captioning model can generate sentences that better reflect user activities at business venues. In our experiments, the business-aware latent topics are effective for adapting to captions to images captured in various businesses than the existing baselines. Moreover, they complement other contextual cues (image, time) in a multi-modal framework.

We previously created the HyperMeeting system to support a chain of geographically and temporally distributed meetings in the form of a hypervideo. This paper focuses on playback plans that guide users through the recorded meeting content by automatically following available hyperlinks. Our system generates playback plans based on users' interests or prior meeting attendance and presents a dialog that lets users select the most appropriate plan. Prior experience with playback plans revealed users' confusion with automatic link following within a sequence of meetings. To address this issue, we designed three timeline visualizations of playback plans. A user study comparing the timeline designs indicated that different visualizations are preferred for different tasks, making switching among them important. The study also provided insights that will guide research of personalized hypervideo, both inside and outside a meeting context.

It is difficult to adjust the content of traditional slide presentations to the knowledge level, interest and role of individuals. This might force presenters to include content that is irrelevant for part of the audience, which negatively affects the knowledge transfer of the presentation. In this work, we present a prototype that is able to eliminate non-pertinent information from slides by presenting annotations for individual attendees on optical head-mounted displays. We first create guidelines for creating optimal annotations by evaluating several types of annotations alongside different types of slides. Then we evaluate the knowledge acquisition of presentation attendees using the prototype versus traditional presentations. Our results show that annotations with a limited amount of information, such as text up to 5 words, can significantly increase the amount of knowledge gained from attending a group presentation. Additionally, presentations where part of the information is moved to annotations are judged more positively on attributes such as clarity and enjoyment.

WSICC has established itself as a truly interactive workshop at EuroITV'13, TVX'14, and TVX'15 with three successful editions. The fourth edition of the WSICC workshop aims to bring together researchers and practitioners working on novel approaches for interactive multimedia content consumption. New technologies, devices, media formats, and consumption paradigms are emerging that allow for new types of interactivity. Examples include multi-panoramic video and object-based audio, increasingly available in live scenarios with content feeds from a multitude of sources. All these recent advances have an impact on different aspects related to interactive content consumption, which the workshop categorizes into Enabling Technologies, Content, User Experience, and User Interaction. The resources from past editions of the workshop are available on the http://wsicc.net website.

Hypervideo usage scenarios like physiotherapy trainings or instructions for manual tasks make it hard for users to use an input device like a mouse or touch screen on a hand-held device while they are performing an exercise or use both hands to perform a manual task. In this work, we are trying to overcome this issue by providing an alternative input method for hypervideo navigation using speech commands. In a user test, we evaluated two different speech recognition libraries, annyang (in combination with the Web Speech API) and PocketSphinx.js (in combination with the Web Audio API), for their usability to control hypervideo players. Test users spoke 18 words, either in German or English, which were recorded and then processed by both libraries. We found out that annyang shows better recognition results. However, depending on other factors of influence, like the occurrence of background noise (reliability), the availability of an internet connection, or the used browser, PocketSphinx.js may be a better fit.

Hypervideo based physiotherapy trainings bear an opportunity to support patients in continuing their training after being released from a rehabilitation clinic. Many exercises require the patient to sit on the floor or a gymnastic ball, lie on a gymnastics mat, or do the exercises in other postures. Using a laptop or tablet with a stand to show the exercises is more helpful than for example just having some drawings on a leaflet. However, it may lead to incorrect execution of the exercises while maintaining eye contact with the screen or require the user to get up and select the next exercise if the devices is positioned for a better view. A dual screen application, where contents are shown on a TV screen and the flow of the video can be controlled from a mobile second device, allows patients to keep their correct posture and the same time view and select contents. In this paper we propose first studies for user interface designs for such apps. Initial paper prototypes are discussed and refined in two focus groups. The results are then presented to a broader range of users in a survey. Three prototypes for the mobile app and one prototype for the TV are identified for future user tests.

The creation of hypervideos usually requires a lot of planning and is time consuming with respect to media content creation. However, when structure and media are put together to author a hypervideo, it may only require minor changes to make the hypervideo available in other languages or for another user group (like beginners versus experts). However, to make the translation of media and all navigation elements of a hypervideo efficient and manageable, the authoring tool needs a GUI that provides a good overview of elements that can be translated and of missing translations. In this work, we propose screen concepts that help authors to provide different versions (for example language and/or experience level) of a hypervideo. We analyzed different variants of GUI elements and evaluated them in a survey. We draw guidelines from the results that can help with the creation of similar systems in the future.

The confluence of technologies such as telepresence, immersive imaging, model based virtual mirror worlds, mobile live streaming, etc. give rise to a capability for people anywhere to view and connect with present or past events nearly anywhere on earth. This capability properly belongs to a public commons, available as a birthright of all humans, and can been seen as part of an evolutionary transition supporting a global collective mind. We describe examples and elements of this capability, and suggest how they can be better integrated through a tool we call TeleViewer and a framework called WorldViews, which supports easy sharing of views as well as connecting of providers and consumers of views all around the world.

Most current mobile and wearable devices are equipped with inertial measurement units (IMU) that allow the detection of motion gestures, which can be used for interactive applications. A difficult problem to solve, however, is how to separate ambient motion from an actual motion gesture input. In this work, we explore the use of motion gesture data labeled with gesture execution phases for training supervised learning classifiers for gesture segmentation. We believe that using gesture execution phase data can significantly improve the accuracy of gesture segmentation algorithms. We define gesture execution phases as the start, middle and end of each gesture. Since labeling motion gesture data with gesture execution phase information is work intensive, we used crowd workers to perform the labeling. Using this labeled data set, we trained SVM-based classifiers to segment motion gestures from ambient movement of the device t. We describe initial results that indicate that gesture execution phase can be accurately recognized by SVM classifiers. Our main results show that training gesture segmentation classifiers with phase-labeled data substantially increases the accuracy of gesture segmentation: we achieved a gesture segmentation accuracy of 0.89 for simulated online segmentation using a sliding window approach.