In real settings, natural body movements can be erroneously recognized by whole-body input systems as explicit input actions. We call body activity not intended as input actions "background activity." We argue that understanding background activity is crucial to the success of always-available whole-body input in the real world. To operationalize this argument, we contribute a reusable study methodology and software tools to generate standardized background activity datasets composed of data from multiple Kinect cameras, a Vicon tracker, and two high-definition video cameras. Using our methodology, we create an example background activity dataset for a television-oriented living room setting. We use this dataset to demonstrate how it can be used to redesign a gestural interaction vocabulary to minimize conflicts with the real world. The software tools and initial living room dataset are publicly available (this http URL).
LightGuide is a system that explores a new approach to gesture guidance where we project guidance hints directly on a user's body. These projected hints guide the user in completing the desired motion with their body part which is particularly useful for performing movements that require accuracy and proper technique, such as during exercise or physical therapy. Our proof-of-concept implementation consists of a single low-cost depth camera and projector and we present four novel interaction techniques that are focused on guiding a user's hand in mid-air. Our visualizations are designed to incorporate both feedback and feedforward cues to help guide users through a range of movements. We quantify the performance of LightGuide in a user study comparing each of our on-body visualizations to hand animation videos on a computer display in both time and accuracy. Exceeding our expectations, participants performed movements with an average error of 21.6mm, nearly 85 more accurately than when guided by video. Action recognition has become a very important topic in computer vision, with many fundamental applications, in robotics, video surveillance, human-computer interaction, and multimedia retrieval among others and a large variety of approaches have been described. The purpose of this survey is to give an overview and categorization of the approaches used. We concentrate on approaches that aim on classification of full-body motions, such as kicking, punching, and waving, and we categorize them according to how they represent the spatial and temporal structure of actions; how they segment actions from an input stream of visual data; and how they learn a view-invariant representation of actions. Human activity recognition is an important area of computer vision research. Its applications include surveillance systems, patient monitoring systems, and a variety of systems that involve interactions between persons and electronic devices such as human-computer interfaces. Most of these applications require an automated recognition of high-level activities, composed of multiple simple (or atomic) actions of persons. This article provides a detailed overview of various state-of-the-art research papers on human activity recognition. We discuss both the methodologies developed for simple human actions and those for high-level activities. An approach-based taxonomy is chosen that compares the advantages and limitations of each approach. Recognition methodologies for an analysis of the simple actions of a single person are first presented in the article. Space-time volume approaches and sequential approaches that represent and recognize activities directly from input images are discussed. Next, hierarchical recognition methodologies for high-level activities are presented and compared. Statistical approaches, syntactic approaches, and description-based approaches for hierarchical recognition are discussed in the article. In addition, we further discuss the papers on the recognition of human-object interactions and group activities. Public datasets designed for the evaluation of the recognition methodologies are illustrated in our article as well, comparing the methodologies' performances. This review will provide the impetus for future research in more productive areas. 3D interfaces use motion sensing, physical input, and spatial interaction techniques to effectively control highly dynamic virtual content. Now, with the advent of the Nintendo Wii, Sony Move, and Microsoft Kinect, game developers and researchers must create compelling interface techniques and game-play mechanics that make use of these technologies. At the same time, it is becoming increasingly clear that emerging game technologies are not just going to change the way we play games, they are also going to change the way we make and view art, design new products, analyze scientific datasets, and more. This introduction to 3D spatial interfaces demystifies the workings of modern videogame motion controllers and provides an overview of how it is used to create 3D interfaces for tasks such as 2D and 3D navigation, object selection and manipulation, and gesture-based application control. Topics include the strengths and limitations of various motion-controller sensing technologies in today's peripherals, useful techniques for working with these devices, and current and future applications of these technologies to areas beyond games. The course presents valuable information on how to utilize existing 3D user-interface techniques with emerging technologies, how to develop interface techniques, and how to learn from the successes and failures of spatial interfaces created for a variety of application domains. Depth cameras have become a fixture of millions of living rooms thanks to the Microsoft Kinect. Yet to be seen is whether they can succeed as widely in other areas of the home. This research takes the Kinect into real-life kitchens, where touchless gestural control could be a boon for messy hands, but where commands are interspersed with the movements of cooking. We implement a recipe navigator, timer and music player and, experimentally, allow users to change the control scheme at runtime and navigate with other limbs when their hands are full. We tested our system with five subjects who baked a cookie recipe in their own kitchens, and found that placing the Kinect was simple and that subjects felt successful. However, testing in real kitchens underscored the challenge of preventing accidental commands in tasks with sporadic input. Vision-based human action recognition is the process of labeling image sequences with action labels. Robust solutions to this problem have applications in domains such as visual surveillance, video retrieval and human-computer interaction. The task is challenging due to variations in motion performance, recording settings and inter-personal differences. In this survey, we explicitly address these challenges. We provide a detailed overview of current advances in the field. Image representations and the subsequent classification process are discussed separately to focus on the novelties of recent research. Moreover, we discuss limitations of the state of the art and outline promising directions of research. This paper presents an application that uses hand gesture input to control a computer while giving a presentation. In order to develop a prototype of this application, we have defined an interaction model, a notation for gestures, and a set of guidelines to design gestural command sets. This works aims to define interaction styles that work in computerized reality environments. In our application, gestures are used for interacting with the computer as well as for communicating with other people or operating other devices. Instrumented with a single depth camera, a stereoscopic projector, and a curved screen, MirageTable is an interactive system designed to merge real and virtual worlds into a single spatially registered experience on top of a table. Our depth camera tracks the user's eyes and performs a real-time capture of both the shape and the appearance of any object placed in front of the camera (including user's body and hands). This real-time capture enables perspective stereoscopic 3D visualizations to a single user that account for deformations caused by physical objects on the table. In addition, the user can interact with virtual objects through physically-realistic freehand actions without any gloves, trackers, or instruments. We illustrate these unique capabilities through three application examples: virtual 3D model creation, interactive gaming with real and virtual objects, and a 3D teleconferencing experience that not only presents a 3D view of a remote person, but also a seamless 3D shared task space. We also evaluated the user's perception of projected 3D objects in our system, which confirmed that the users can correctly perceive such objects even when they are projected over different background colors and geometries (e.g., gaps, drops).  HoloDesk is an interactive system combining an optical see through display and Kinect camera to create the illusion that users are directly interacting with 3D graphics. A virtual image of a 3D scene is rendered through a half silvered mirror and spatially aligned with the real-world for the viewer. Users easily reach into an interaction volume displaying the virtual image. This allows the user to literally get their hands into the virtual display and to directly interact with an spatially aligned 3D virtual world, without the need for any specialized head-worn hardware or input device. We introduce a new technique for interpreting raw Kinect data to approximate and track rigid (e.g., books, cups) and non-rigid (e.g., hands, paper) physical objects and support a variety of physics-inspired interactions between virtual and real. In particular the algorithm models natural human grasping of virtual objects with more fidelity than previously demonstrated. A qualitative study highlights rich emergent 3D interactions, using hands and real-world objects. The implementation of HoloDesk is described in full, and example application scenarios explored. Finally, HoloDesk is quantitatively evaluated in a 3D target acquisition task, comparing the system with indirect and glasses-based variants. We present a first study of using RGB-D (Kinect-style) cameras for fine-grained recognition of kitchen activities. Our prototype system combines depth (shape) and color (appearance) to solve a number of perception problems crucial for smart space applications: locating hands, identifying objects and their functionalities, recognizing actions and tracking object state changes through actions. Our proof-of-concept results demonstrate great potentials of RGB-D perception: without need for instrumentation, our system can robustly track and accurately recognize detailed steps through cooking activities, for instance how many spoons of sugar are in a cake mix, or how long it has been mixing. A robust RGB-D based solution to fine-grained activity recognition in real-world conditions will bring the intelligence of pervasive and interactive systems to the next level.