Visualizing the History of Living Spaces

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.

[1]  Patrick Bouthemy,et al.  Determining a Structured Spatio-Temporal Representation of Video Content for Efficient Visualization and Indexing , 1998, ECCV.

[2]  Robert Kincaid,et al.  Line graph explorer: scalable display of line graphs using Focus+Context , 2006, AVI '06.

[3]  Gennady L. Andrienko,et al.  Interactive analysis of event data using space-time cube , 2004, Proceedings. Eighth International Conference on Information Visualisation, 2004. IV 2004..

[4]  Ben Shneiderman,et al.  LifeLines: visualizing personal histories , 1996, CHI.

[5]  Donald G. Janelle,et al.  Geovisualization of Human Activity Patterns Using 3 D GIS : A Time-Geographic Approach , 2002 .

[6]  Marcel Worring,et al.  The Semantic Pathfinder: Using an Authoring Metaphor for Generic Multimedia Indexing , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  William Wright,et al.  GeoTime Information Visualization , 2004, IEEE Symposium on Information Visualization.

[8]  R. Brunelli,et al.  A Survey on the Automatic Indexing of Video Data, , 1999, J. Vis. Commun. Image Represent..

[9]  Paul A. Viola,et al.  Detecting Pedestrians Using Patterns of Motion and Appearance , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[10]  Christopher Richard Wren,et al.  Tracking people in mixed modality systems , 2007, Electronic Imaging.

[11]  Christopher R. Wren,et al.  Toward Spatial Queries for Spatial Surveillance Tasks , 2006 .

[12]  Martin Wattenberg Baby names, visualization, and social data analysis , 2005 .