Pok 'emon Go has received unprecedented media coverage for a location-based game that uses augmented reality techniques. The game has been commonly associated with greater access to public spaces, increasing the number of people out on the streets, and generally improving health, social, and security indices. However, the true impact of Pok 'emon Go on people's mobility patterns in a city is still largely unknown. In this paper we perform a natural experiment using data from mobile networks to evaluate the effect of Pok 'emon Go on the pulse of a big city: Santiago of Chile. We found a significant effect of Pok 'emon Go on the floating population of Santiago: up to 13.8 more people being outside at certain times, even if they do not seem to go out of their usual way. These effects at specific times were found by performing several regressions using count models over snapshots of the cell phone network. The effect is significant after controlling for land use, daily patterns, and points of interest in the city. Particularly, we found that, in business days, there is more people on the street at commuting times, meaning that people did not change their daily routines but slightly adapted them to play the game. Conversely, on Saturday and Sunday night, people indeed went out to play to places nearby their homes. Even if the statistical effects of the game do not reflect the massive reach portrayed by the media, it still allowed the streets to become a new place for people to spend time in. This is important, because results like these are expected to inform long-term infrastructure investments by city officials, jointly with public policies aimed at, for example, stimulating pedestrian traffic or suggesting alternative routes. Our work supports the notion that location-based games like Pok 'emon Go have benefits for the life in the city.
Augmented Reality and Location-based Games The effects of augmented reality games and applications within the city have been anticipated for more than one decade @cite_41 @cite_49 . However, the limits in mainstream hardware have impeded their general implementation adoption at different times. Most studies about the impact of those games have been small-scale only @cite_24 . Even though smartphone technology has allowed location-based augmented reality games to become more commonplace in the last few years, until the launch of Pokémon Go, they still lacked the cultural impact needed to have a considerable effect on the city. As Frank Lantz is quoted in @cite_16 , in relation to the game PacManhattan: @cite_32 . Since as we discussed above, Pokémon is one of the most successful media franchises in the world @cite_1 , it enables the unique opportunity to study both, the impact of a location-based augmented reality game; and the effect of an intervention at the city scale when it comes to population mobility.
@cite_41 @cite_1 @cite_32 @cite_24 @cite_49 @cite_16
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While the knowledge economy has reshaped the world, schools lag behind in producing appropriate learning for this social change. Science education needs to prepare students for a future world in which multiple representations are the norm and adults are required to “think like scientists.” Location-based augmented reality games offer an opportunity to create a “post-progressive” pedagogy in which students are not only immersed in authentic scientific inquiry, but also required to perform in adult scientific discourses. This cross-case comparison as a component of a design-based research study investigates three cases (roughly 28 students total) where an Augmented Reality curriculum, Mad City Mystery, was used to support learning in environmental science. We investigate whether augmented reality games on handhelds can be used to engage students in scientific thinking (particularly argumentation), how game structures affect students’ thinking, the impact of role playing on learning, and the role of the physical environment in shaping learning. We argue that such games hold potential for engaging students in meaningful scientific argumentation. Through game play, players are required to develop narrative accounts of scientific phenomena, a process that requires them to develop and argue scientific explanations. We argue that specific game features scaffold this thinking process, creating supports for student thinking non-existent in most inquiry-based learning environments. Historically, the play of digital games in public was restricted to certain locations such as arcades and cybercafes. The proliferation of personal, mobile, and digitally networked devices, however, has contributed to the ubiquity of digital games in contemporary culture, making them available for play anywhere, any time. This paper uses two examples to examine this recent shift to consider how play and digital games can contribute to revitalising the urban experience. The first examines the experience of playing digital games in urban cybercafes; and the second discusses the recent popular location–based gaming app for the iPhone, Shadow Cities . By contrasting the types of play that unfold in the situated but highly contingent environments of the cybercafe and the ‘hybrid’ space Shadow Cities’ massively multiplayer world, this paper argues that the historical role of play as the foundation for a vibrant and progressive city life is increasingly at stake in the shift towards pervasive, software–mediated gaming applications. We present a study of a mobile mixed reality game called Can You See Me Now? in which online players are chased through a virtual model of a city by ‘runners’ (professional performers equipped with GPS and WiFi technologies) who have to run through the actual city streets in order to catch the players. We present an ethnographic study of the game as it toured through two different cities and draws upon video recordings of online players, runners, technical support crew, and also on system logs of text communication. Our study reveals the diverse ways in which online players experienced the uncertainties inherent in GPS and WiFi, including being mostly unaware of them, but sometimes seeing them as problems, or treating the as a designed feature of the game, and even occasionally exploiting them within gameplay. In contrast, the runners and technical crew were fully aware of these uncertainties and continually battled against them through an ongoing and distributed process of orchestration. As a result, we encourage designers to deal with such uncertainties as a fundamental characteristic of location-based experiences rather than treating them as exceptions or bugs that might be ironed out in the future. We argue that designers should explicitly consider four potential states of being of a mobile participant: connected and tracked, connected but not tracked, tracked but not connected, and neither connected nor tracked. We then introduce five strategies that might be used to deal with uncertainty in these different states for different kinds of participant: remove it, hide it, manage it, reveal it, and exploit it. Finally, we present proposals for new orchestration interfaces that reveal the ‘seams’ in the underlying technical infrastructure by visualizing the recent performance of GPS and WiFi and predicting the likely future performance of GPS. This paper presents a framework which allows urban planners to navigate and interact with large datasets fused with social feeds in real-time, enhanced by a virtual reality (VR) capability, which further promotes the knowledge discovery process and allows to interact with urban data in natural yet immersive way. A challenge in urban planning is making decisions based on datasets which are many times ambiguous, together with effective use of newly available yet unstructured sources of information like social media. Providing expert users with novel ways of representing knowledge can be beneficial for decision making. Game engines have evolved into capable testbeds for novel visualization and interaction techniques. We therefore explore the possibility of using a modern game engine as a platform for knowledge representation in urban planning and how it can be used to model ambiguity. We also investigate how urban planners can benefit from immersion when it comes to data exploration and knowledge discovery. We apply the concept of using primitives to publicly available transportation datasets and social feeds of New York city, we discuss a gesture-based VR extension of our framework and lastly, we conclude the paper with feedback from expert users in urban planning and with an outlook of future challenges. Planners and social psychologists have suggested that the recognizability of the urban environment is linked to people's socio-economic well-being. We build a web game that puts the recognizability of London's streets to the test. It follows as closely as possible one experiment done by Stanley Milgram in 1972. The game picks up random locations from Google Street View and tests users to see if they can judge the location in terms of closest subway station, borough, or region. Each participant dedicates only few minutes to the task (as opposed to 90 minutes in Milgram's). We collect data from 2,255 participants (one order of magnitude a larger sample) and build a recognizability map of London based on their responses. We find that some boroughs have little cognitive representation; that recognizability of an area is explained partly by its exposure to Flickr and Foursquare users and mostly by its exposure to subway passengers; and that areas with low recognizability do not fare any worse on the economic indicators of income, education, and employment, but they do significantly suffer from social problems of housing deprivation, poor living conditions, and crime. These results could not have been produced without analyzing life off- and online: that is, without considering the interactions between urban places in the physical world and their virtual presence on platforms such as Flickr and Foursquare. This line of work is at the crossroad of two emerging themes in computing research - a crossroad where "web science" meets the "smart city" agenda. Augmented reality has been an active area ofresearch for the last two decades or so. This paper presents acomprehensive review of the recent literature on trackingmethods used in Augmented Reality applications, both forindoor and outdoor environments. After critical discussion ofthe methods used for tracking, the paper identifies limitations ofthe state-of-the-art techniques and suggests potential futuredirections to overcome the bottlenecks. The Atari 2600 games supported in the Arcade Learning Environment [, 2013] all feature a known initial (RAM) state and actions that have deterministic effects. Classical planners, however, cannot be used off-the-shelf as there is no compact PDDL-model of the games, and action effects and goals are not known a priori. Indeed, there are no explicit goals, and the planner must select actions on-line while interacting with a simulator that returns successor states and rewards. None of this precludes the use of blind lookahead algorithms for action selection like breadth-first search or Dijkstra's yet such methods are not effective over large state spaces. We thus turn to a different class of planning methods introduced recently that have been shown to be effective for solving large planning problems but which do not require prior knowledge of state transitions, costs (rewards) or goals. The empirical results over 54 Atari games show that the simplest such algorithm performs at the level of UCT, the state-of-the-art planning method in this domain, and suggest the potential of width-based methods for planning with simulators when factored, compact action models are not available. Recent improvements in image-based localization have produced powerful methods that scale up to the massive 3D models emerging from modern Structure-from-Motion techniques. However, these approaches are too resource intensive to run in real-time, let alone to be implemented on mobile devices. In this paper, we propose to combine the scalability of such a global localization system running on a server with the speed and precision of a local pose tracker on a mobile device. Our approach is both scalable and drift-free by design and eliminates the need for loop closure. We propose two strategies to combine the information provided by local tracking and global localization. We evaluate our system on a large-scale dataset of the historic inner city of Aachen where it achieves interactive framerates at a localization error of less than 50cm while using less than 5MB of memory on the mobile device. A visible area detection method that depends on online available information is proposed.Visual popularity and sentiment are calculated based on images and their accompanying text that people share in social mediaThe visual popularity information is presented in a 3D virtual environment using Unreal engine. Display Omitted This paper proposes to use a geotagged virtual world for the visualization of peoples visual interest and their sentiment as captured from their social network activities. Using mobile devices, people widely share their experiences and the things they find interesting through social networks. We experimentally show that accumulating information over a period of time from multiple social network users allows to efficiently map and visualize popular landmarks as found in cities such as Rome in Italy and Dublin in Ireland. The proposed approach is also sensitive to temporal and spatial events that attract visual attention. We visualize the calculated popularity on 3D virtual cities using game engine technologies. Like virtual reality, augmented reality is becoming an emerging platform in new application areas for museums, edutainment, home entertainment, research, industry, and the art communities using novel approaches which have taken augmented reality beyond traditional eye-worn or hand-held displays. In this book, the authors discuss spatial augmented reality approaches that exploit optical elements, video projectors, holograms, radio frequency tags, and tracking technology, as well as interactive rendering algorithms and calibration techniques in order to embed synthetic supplements into the real environment or into a live video of the real environment. Special Features: - Comprehensive overview - Detailed mathematical equations - Code fragments - Implementation instructions - Examples of Spatial AR displays The authors have put together a preliminary collection of Errata. Updates will be posted to this site as necessary. Recent research has shown that surprisingly rich models of human behavior can be learned from GPS (positional) data. However, most research to date has concentrated on modeling single individuals or aggregate statistical properties of groups of people. Given noisy real-world GPS data, we—in contrast—consider the problem of modeling and recognizing activities that involve multiple related individuals playing a variety of roles. Our test domain is the game of capture the flag—an outdoor game that involves many distinct cooperative and competitive joint activities. We model the domain using Markov logic, a statistical relational language, and learn a theory that jointly denoises the data and infers occurrences of high-level activities, such as capturing a player. Our model combines constraints imposed by the geometry of the game area, the motion model of the players, and by the rules and dynamics of the game in a probabilistically and logically sound fashion. We show that while it may be impossible to directly detect a multi-agent activity due to sensor noise or malfunction, the occurrence of the activity can still be inferred by considering both its impact on the future behaviors of the people involved as well as the events that could have preceded it. We compare our unified approach with three alternatives (both probabilistic and nonprobabilistic) where either the denoising of the GPS data and the detection of the high-level activities are strictly separated, or the states of the players are not considered, or both. We show that the unified approach with the time window spanning the entire game, although more computationally costly, is significantly more accurate. The form factors of handheld computers make them increasingly popular among K-12 educators. Although some compelling examples of educational software for handhelds exist, we believe that the potential of this platform are just being discovered. This paper reviews innovative applications for mobile computing for both education and entertainment purposes, and then proposes a framework for approaching handheld applications we call “augmented reality educational gaming.” We then describe our development process in creating a development platform for augmented reality games that draws from rapid prototyping, learner-centered software, and contemporary game design methodologies. We provide a narrative case study of our development activities spread across five case studies with classrooms, and provide a design narrative explaining this development process and articulate an approach to designing educational software on emerging technology platforms. Pedagogical, design, and technical conclusions and implications are discussed. In current games, entire cities can be rendered in real time into massive virtual worlds. In addition to the enormous details of geometry, rendering, effects (e.g., particles), sound effects, and so on, nonplayable characters must also be animated and rendered, and they must interact with the environment and among themselves. Indeed, the computation time of all such data is expensive. Consequently, game designers should define priorities so that more resources can be allocated to generate better graphics, setting aside behavioral aspects. In huge environments, some of the actions behaviors that should be processed can be nonvisible to the players (occluded) or even visible but far away. Normally, in such cases, the common decision is to turn off such processing. However, hidden enemy behaviors that are not processed can result in nonrealistic feedback to the player. In this article, we aim to provide a method to preserve the motion of nonvisible characters while maintaining a compromise with the needed computational time of background behaviors. We apply this idea specifically in crowd collision behavior, proposing nonavoiding collision crowds. Such crowds do not have collision avoidance behaviors but preserve their motion as typical crowds. We propose a mathematical technique to describe how people are affected by others, so collision avoidance methods are not necessarily computed (they can be turned off, which leads to a reduction in the required computational time). Results show that our method replicates the behavior well (velocities, densities, and time) when compared to a free-of-collision method. The success of deep learning in computer vision is based on the availability of large annotated datasets. To lower the need for hand labeled images, virtually rendered 3D worlds have recently gained popularity. Unfortunately, creating realistic 3D content is challenging on its own and requires significant human effort. In this work, we propose an alternative paradigm which combines real and synthetic data for learning semantic instance segmentation and object detection models. Exploiting the fact that not all aspects of the scene are equally important for this task, we propose to augment real-world imagery with virtual objects of the target category. Capturing real-world images at large scale is easy and cheap, and directly provides real background appearances without the need for creating complex 3D models of the environment. We present an efficient procedure to augment these images with virtual objects. In contrast to modeling complete 3D environments, our data augmentation approach requires only a few user interactions in combination with 3D models of the target object category. Leveraging our approach, we introduce a novel dataset of augmented urban driving scenes with 360 degree images that are used as environment maps to create realistic lighting and reflections on rendered objects. We analyze the significance of realistic object placement by comparing manual placement by humans to automatic methods based on semantic scene analysis. This allows us to create composite images which exhibit both realistic background appearance as well as a large number of complex object arrangements. Through an extensive set of experiments, we conclude the right set of parameters to produce augmented data which can maximally enhance the performance of instance segmentation models. Further, we demonstrate the utility of the proposed approach on training standard deep models for semantic instance segmentation and object detection of cars in outdoor driving scenarios. We test the models trained on our augmented data on the KITTI 2015 dataset, which we have annotated with pixel-accurate ground truth, and on the Cityscapes dataset. Our experiments demonstrate that the models trained on augmented imagery generalize better than those trained on fully synthetic data or models trained on limited amounts of annotated real data. The availability of commodity depth sensors such as Kinect has enabled development of methods which can densely reconstruct arbitrary scenes. While the results of these methods are accurate and visually appealing, they are quite often incomplete. This is either due to the fact that only part of the space was visible during the data capture process or due to the surfaces being occluded by other objects in the scene. In this paper, we address the problem of completing and refining such reconstructions. We propose a method for scene completion that can infer the layout of the complete room and the full extent of partially occluded objects. We propose a new probabilistic model, Contour Completion Random Fields, that allows us to complete the boundaries of occluded surfaces. We evaluate our method on synthetic and real world reconstructions of 3D scenes and show that it quantitatively and qualitatively outperforms standard methods. We created a large dataset of partial and complete reconstructions which we will make available to the community as a benchmark for the scene completion task. Finally, we demonstrate the practical utility of our algorithm via an augmented-reality application where objects interact with the completed reconstructions inferred by our method. In many real tasks the features are evolving, with some features being vanished and some other features augmented. For example, in environment monitoring some sensors expired whereas some new ones deployed; in mobile game recommendation some games dropped whereas some new ones added. Learning with such incremental and decremental features is crucial but rarely studied, particularly when the data coming like a stream and thus it is infeasible to keep the whole data for optimization. In this paper, we study this challenging problem and present the OPID approach. Our approach attempts to compress important information of vanished features into functions of survived features, and then expand to include the augmented features. It is the one-pass learning approach, which only needs to scan each instance once and does not need to store the whole data, and thus satisfy the evolving streaming data nature. The effectiveness of our approach is validated theoretically and empirically. Over the past few years, virtual reality has experienced a resurgence. Fueled by a proliferation of consumer-level head-mounted display and motion tracking devices, an unprecedented quantity of immersive experiences and content are available for both desktop and mobile platforms. However, natural locomotion in immersive virtual environments remains a significant challenge. Many of the VR applications available to date require seated use or limit body movement within a small area, instead relying a gamepad or mouse keyboard for movement within the virtual world. Lacking support for natural walking, these virtual reality experiences do not fully replicate the physical and perceptual cues from the real world, and often fall short in maintaining the illusion that the user has been transported to another place. We present a virtual reality demonstration that supports infinite walking within a confined physical space. This is achieved using redirected walking, a class of techniques that introduce subtle discrepancies between physical and virtual motions [ 2001]. When employed properly, redirected walking can be stunningly effective. Previous research has made users believe that they walked in a straight line when they actually traveled in a wide circle, or that they walked between waypoints in a long virtual hallway when in fact they went back and forth between the same two points in the real world. While perceptually compelling, redirected walking is challenging to employ effectively in an unconstrained scenario because users' movements may often be unpredictable. Therefore, our recent research has focused on dynamic planning and optimization of redirected walking techniques, enabling the system to intelligently apply redirection as users explore virtual environments of arbitrary size and shape [ 2014b] [ 2014a]. In this Emerging Technologies exhibit, attendees will explore a large-scale, outdoor immersive virtual environment in a head-mounted display (see Figure 1). The demonstration will support natural walking within a physical area of at least 6x6m, using a wide-area motion tracking system provided by PhaseSpace Inc. The virtual reality scenario will instruct users to scout the environment while stopping to take panoramic photos at various locations in the virtual world. As users explore the environment, our automated planning algorithm will dynamically apply redirection to optimally steer them away from the physical boundaries of the exhibit, thus enabling the experience of limitless walking in a potentially infinite virtual world (see Figure 2). Human Pacman is an interactive ubiquitous and mobile entertainment system that is built upon position and perspective sensing via Global Positioning System and inertia sensors; and tangible human-computer interfacing with the use of Bluetooth and capacitive sensors. Although these sensing-based subsystems are weaved into the fabric of the game and are therefore translucent to players, they are nevertheless the technical enabling forces behind Human Pacman. The game strives to bring the computer gaming experience to a new level of emotional and sensory gratification by embedding the natural physical world ubiquitously and seamlessly with a fantasy virtual playground. We have progressed from the old days of 2D arcade Pacman on screens, with incremental development, to the popular 3D game console Pacman, and the recent mobile online Pacman. With our novel Human Pacman, we have a physical role-playing computer fantasy together with real human-social and mobile-gaming that emphasizes on collaboration and competition between players in a wide outdoor physical area that allows natural wide-area human-physical movements. Pacmen and Ghosts are now real human players in the real world experiencing mixed computer graphics fantasy-reality provided by using wearable computers that are equipped with GPS and inertia sensors for players' position and perspective tracking. Virtual cookies and actual tangible physical objects with Bluetooth devices and capacitive sensors are incorporated into the game play to provide novel experiences of seamless transitions between real and virtual worlds. In short, we believe Human Pacman is pioneering a new form of gaming that is based on sensing technology and anchored on physicality, mobility, social interaction, and ubiquitous computing. This paper examines the literature on computer games and serious games in regard to the potential positive impacts of gaming on users aged 14 years or above, especially with respect to learning, skill enhancement and engagement. Search terms identified 129 papers reporting empirical evidence about the impacts and outcomes of computer games and serious games with respect to learning and engagement and a multidimensional approach to categorizing games was developed. The findings revealed that playing computer games is linked to a range of perceptual, cognitive, behavioural, affective and motivational impacts and outcomes. The most frequently occurring outcomes and impacts were knowledge acquisition content understanding and affective and motivational outcomes. The range of indicators and measures used in the included papers are discussed, together with methodological limitations and recommendations for further work in this area. In many critical applications such as airport operations (for capacity planning), military simulations (for tactical training and planning), and medical simulations (for the planning of medical treatment and surgical operations), it is very useful to conduct simulations within physically accurate and visually realistic settings that are represented by real video imaging sequences. Furthermore, it is important that the simulated entities conduct autonomous actions which are realistic and which follow plans of action or intelligent behavior in reaction to current situations. We describe the research we have conducted to incorporate synthetic objects in a visually realistic manner in video sequences representing a real scene. We also discuss how the synthetic objects can be designed to conduct intelligent behavior within an augmented reality setting. The paper discusses both the computer vision aspects that we have addressed and solved, and the issues related to the insertion of intelligent autonomous objects within an augmented reality simulation.