In 1997, Azuma published a survey on augmented reality (AR). Our goal is to complement, rather than replace, the original survey by presenting representative examples of the new advances. We refer one to the original survey for descriptions of potential applications (such as medical visualization, maintenance and repair of complex equipment, annotation, and path planning); summaries of AR system characteristics (such as the advantages and disadvantages of optical and video approaches to blending virtual and real, problems in display focus and contrast, and system portability); and an introduction to the crucial problem of registration, including sources of registration error and error-reduction strategies.

https://www.cc.gatech.edu/~blair/papers/ARsurveyCGA.pdf

Recent advances in augmented reality

Visual surveillance in dynamic scenes, especially for humans and vehicles, is currently one of the most active research topics in computer vision. It has a wide spectrum of promising applications, including access control in special areas, human identification at a distance, crowd flux statistics and congestion analysis, detection of anomalous behaviors, and interactive surveillance using multiple cameras, etc. In general, the processing framework of visual surveillance in dynamic scenes includes the following stages: modeling of environments, detection of motion, classification of moving objects, tracking, understanding and description of behaviors, human identification, and fusion of data from multiple cameras. We review recent developments and general strategies of all these stages. Finally, we analyze possible research directions, e.g., occlusion handling, a combination of twoand three-dimensional tracking, a combination of motion analysis and biometrics, anomaly detection and behavior prediction, content-based retrieval of surveillance videos, behavior understanding and natural language description, fusion of information from multiple sensors, and remote surveillance.

/pdf/a-survey-on-visual-surveillance-of-object-motion-and-302zsaqo09.pdf

A survey on visual surveillance of object motion and behaviors

A scheme is developed for classifying the types of motion perceived by a humanlike robot. It is assumed that the robot receives visual images of the scene using a perspective system model. Equations, theorems, concepts, clues, etc., relating the objects, their positions, and their motion to their images on the focal plane are presented. >

http://ieeexplore.ieee.org/iel2/815/3148/00100651.pdf

Robot vision

Gesture recognition pertains to recognizing meaningful expressions of motion by a human, involving the hands, arms, face, head, and/or body. It is of utmost importance in designing an intelligent and efficient human-computer interface. The applications of gesture recognition are manifold, ranging from sign language through medical rehabilitation to virtual reality. In this paper, we provide a survey on gesture recognition with particular emphasis on hand gestures and facial expressions. Applications involving hidden Markov models, particle filtering and condensation, finite-state machines, optical flow, skin color, and connectionist models are discussed in detail. Existing challenges and future research possibilities are also highlighted

/pdf/gesture-recognition-a-survey-3j4b8mpnm4.pdf

Gesture Recognition: A Survey

The proliferation of computing into the physical world promises more than the ubiquitous availability of computing infrastructure; it suggest new paradigms of interaction inspired by constant access to information and computational capabilities. For the past decade, application-driven research on abiquitous computing (ubicomp) has pushed three interaction themes:natural interfaces, context-aware applications,andautomated capture and access. To chart a course for future research in ubiquitous computing, we review the accomplishments of these efforts and point to remaining research challenges. Research in ubiquitious computing implicitly requires addressing some notion of scale, whether in the number and type of devices, the physical space of distributed computing, or the number of people using a system. We posit a new area of applications research, everyday computing, focussed on scaling interaction with respect to time. Just as pushing the availiability of computing away from the traditional desktop fundamentally changes the relationship between humans and computers, providing continuous interaction moves computing from a localized tool to a constant companion. Designing for continous interaction requires addressing interruption and reumption of intreaction, representing passages of time and providing associative storage models. Inherent in all of these interaction themes are difficult issues in the social implications of ubiquitous computing and the challenges of evaluating> ubiquitious computing research. Although cumulative experience points to lessons in privacy, security, visibility, and control, there are no simple guidelines for steering research efforts. Akin to any efforts involving new technologies, evaluation strategies form a spectrum from technology feasibility efforts to long-term use studies—but a user-centric perspective is always possible and necessary

/pdf/charting-past-present-and-future-research-in-ubiquitous-2aoexf7fgc.pdf

Charting past, present, and future research in ubiquitous computing

We present two real-time hidden Markov model-based systems for recognizing sentence-level continuous American sign language (ASL) using a single camera to track the user's unadorned hands. The first system observes the user from a desk mounted camera and achieves 92 percent word accuracy. The second system mounts the camera in a cap worn by the user and achieves 98 percent accuracy (97 percent with an unrestricted grammar). Both experiments use a 40-word lexicon.

/pdf/real-time-american-sign-language-recognition-using-desk-and-56a932db3b.pdf

Real-time American sign language recognition using desk and wearable computer based video

This paper describes what we see as fundamental difficulties in both the pure ubiquitous computing and pure wearable computing paradigms when applied to context-aware applications. In particular, ubiquitous computing and smart room systems tend to have difficulties with privacy and personalization, while wearable systems have trouble with localized information, localized resource control, and resource management between multiple people. These difficulties are discussed and a peer-to-peer network of wearable and ubiquitous computing components is proposed as a solution. This solution is demonstrated through several implemented applications.

Wearable computing meets ubiquitous computing: reaping the best of both worlds

We propose a practical application of wearable computing and augmented reality which enhances the game of billiards. A vision algorithm is implemented which operates in interactive-time with the user to assist planning and aiming. Probabilistic color models and symmetry operations are used to localize the table, pockets and balls through a video camera near the user's eye. Classification of the objects of interest is performed and each possible shot is ranked in order to determine its relative usefulness. The system allows the user to proceed through a regular pool game while it automatically determines strategic shots. The resulting trajectories are rendered as graphical overlays on a head mounted live video display. The wearable video output and the computer vision system provide an integration of real and virtual environments which enhances the experience of playing and learning the game of billiards without encumbering the player.

Stochasticks: augmenting the billiards experience with probabilistic vision and wearable computers

Modern wearable computer designs package workstation level performance in systems small enough to be worn as clothing. These machines enable technology to be brought where it is needed the most for the handicapped: everyday mobile environments. This paper describes a research effort to make a wearable computer that can recognize (with the possible goal of translating) sentence level American Sign Language (ASL) using only a baseball cap mounted camera for input. Current accuracy exceeds 97% per word on a 40 word lexicon.

A Wearable Computer Based American Sign Language Recognizer

Modern wearable computer designs package work-station level performance in systems small enough to be worn as clothing. These machines enable technology to be brought where it is needed the most for the handicapped: everyday mobile environments. This paper describes a research effort to make a wearable computer that can recognize (with the possible goal of translating) sentence level American Sign Language (ASL) using only a baseball cap mounted camera for input. Current accuracy exceeds 97% per word on a 40 word lexicon.

Joshua Weaver

Papers

Real-time American sign language recognition using desk and wearable computer based video

Wearable computing meets ubiquitous computing: reaping the best of both worlds

Stochasticks: augmenting the billiards experience with probabilistic vision and wearable computers

A Wearable Computer Based American Sign Language Recognizer

A wearable computer based American sign language recognizer