G+ Ripples is a visualization of information flow that shows users how public posts are shared on Google+. Unlike other social network visualizations, Ripples exists as a "native" visualization: it is directly accessible from public posts on Google+. This unique position leads to both new constraints and new possibilities for design. We describe the visualization technique, which is a new mix of node-and-link and circular treemap metaphors. We then describe user reactions as well as some of the patterns of sharing that are made evident by the Ripples visualization.

One prediction about the future of pervasive technology is that people will carry the tools needed to interface with technological resources sprinkled throughout the environment. A problem with this vision is the dark side of the network effect: early adopters will end up carrying around interfaces for technology that largely does not yet exist, and building managers will question the value of installing technology with features that almost no one will be able to use. An intermediate solution is that certain buildings with specific needs for efficiency or security (such as hospitals) may become smart, with technology insinuated into particular spaces. Since many, or even most of the people in these spaces will not have the technology to interface directly with the new pervasive resources, we must think of the interaction idiom as initially being closer to the notion of smart environments. These environments will have to sense, interpret, and facilitate the actions of the inhabitants, possibly with very little help from technology attached to the people involved, or even their cooperation. We survey a body of work on perceptual tools for smart buildings, built on the sensor network model, and focused on the idea that statistical methods and population dynamics can provide valuable information even in situations where detection of individual instances of behavior may be difficult to detect. These are some of the tools which will fuel the building optimization applications that will justify the efforts of early adopters to build smart buildings studded with pervasive technology.

No abstract available.

In traditional surveillance systems tracking of objects is achieved by means of image and video processing. The disadvantages of such surveillance systems is that if an object needs to be tracked - it has to be observed by a video camera. However, geometries of indoor spaces typically require a large number of video cameras to provide the coverage necessary for robust operation of video-based tracking algorithms. Increased number of video streams increases the computational burden on the surveillance system in order to obtain robust tracking results. In this paper we present an approach to tracking in mixed modality systems, with a variety of sensors. The system described here includes over 200 motion sensors as well as 6 moving cameras. We track individuals in the entire space and across cameras using contextual information available from the motion sensors. Motion sensors allow us to almost instantaneously find plausible tracks in a very large volume of data, ranging in months, which for traditional video search approaches could be virtually impossible. We describe a method that allows us to evaluate when the tracking system is unreliable and present the data to a human operator for disambiguation.

The technology available to building designers now makes it possible to monitor buildings on a very large scale. Video cameras and motion sensors are commonplace in practically every office space, and are slowly making their way into living spaces. The application of such technologies, in particular video cameras, while improving security, also violates privacy. On the other hand, motion sensors, while being privacy-conscious, typically do not provide enough information for a human operator to maintain the same degree of awareness about the space that can be achieved by using video cameras. We propose a novel approach in which we use a large number of simple motion sensors and a small set of video cameras to monitor a large office space. In our system we deployed 215 motion sensors and six video cameras to monitor the 3,000-square-meter office space occupied by 80 people for a period of about one year. The main problem in operating such systems is finding a way to present this highly multidimensional data, which includes both spatial and temporal components, to a human operator to allow browsing and searching recorded data in an efficient and intuitive way. In this paper we present our experiences and the solutions that we have developed in the course of our work on the system. We consider this work to be the first step in helping designers and managers of building systems gain access to information about occupants' behavior in the context of an entire building in a way that is only minimally intrusive to the occupants' privacy.

Human motion can be understood on many levels. The most basic level is the notion that humans are collections of things that have predictable visual appearance. Next is the notion that humans exist in a physical universe, as a consequence of this, a large part of human motion can be modeled and predicted with the laws of physics. Finally there is the notion that humans utilize muscles to actively shape purposeful motion. We employ a recursive framework for real-time, 3D tracking of human motion that enables pixel-level, probabilistic processes to take advantage of the contextual knowledge encoded in the higher-level models, including models of dynamic constraints on human motion. We show that models of purposeful action arise naturally from this framework, and further, that those models can be used to improve the perception of human motion. Results are shown that demonstrate automatic discovery of features in this new feature space.

Pfinder is a real-time system for tracking people and interpreting their behavior. It runs at 10 Hz on a standard SGI Indy computer, and has performed reliably on thousands of people in many different physical locations. The system uses a multiclass statistical model of color and shape to obtain a 2D representation of head and hands in a wide range of viewing conditions. Pfinder has been successfully used in a wide range of applications including wireless interfaces, video databases, and low-bandwidth coding.

Pfinder is a real-time system for tracking and interpretation of people. It runs on a standard SGI Indy computer, and has performed reliably on thousands of people in many different physical locations. The system uses a multi-class statistical model of color and shape to obtain a 2-D representation of head and hands in a wide range of viewing conditions. These representations are useful for applications such as wireless interfaces, video databases, and low-bandwidth coding, without cumbersome wires or attached sensors.