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.

A predictive tool to simulate human visual search behavior would help interface designers inform and validate their design. Such a tool would benefit from a semantic component that would help predict search behavior even in the absence of exact textual matches between goal and target. This paper discusses a comparison of three semantic systems-LSA, WordNet and PMI-IR-to evaluate their performance in predicting the link that people would select given an information goal and a webpage. PMI-IR best predicted human performance as observed in a user study.