John Krumm

Wallflower: principles and practice of background maintenance

Background maintenance is a frequent element of video surveillance systems. We develop Wallflower, a three-component system for background maintenance: the pixel-level component performs Wiener filtering to make probabilistic predictions of the expected background; the region-level component fills in homogeneous regions of foreground objects; and the frame-level component detects sudden, global changes in the image and swaps in better approximations of the background. We compare our system with 8 other background subtraction algorithms. Wallflower is shown to outperform previous algorithms by handling a greater set of the difficult situations that can occur. Finally, we analyze the experimental results and propose normative principles for background maintenance.

EasyLiving: Technologies for Intelligent Environments

The EasyLiving project is concerned with development of an architecture and technologies for intelligent environments which allow the dynamic aggregation of diverse I/O devices into a single coherent user experience. Components of such a system include middleware (to facilitate distributed computing), world modelling (to provide location-based context), perception (to collect information about world state), and service description (to support decomposition of device control, internal logic, and user interface). This paper describes the current research in each of these areas, highlighting some common requirements for any intelligent environment.

Multi-camera multi-person tracking for EasyLiving

While intelligent environments are often cited as a reason for doing work on visual person-tracking, really making an intelligent environment exposes many real-world problems in visual tracking that must be solved to make the technology practical. In the context of our EasyLiving project in intelligent environments, we created a practical person-tracking system that solves most of the real-world problems. It uses two sets of color stereo cameras for tracking multiple people during live demonstrations in a living room. The stereo images are used for locating people, and the color images are used for maintaining their identities. The system runs quickly enough to make the room feel responsive, and it tracks multiple people standing, walking, sitting, occluding, and entering and leaving the space.

A survey of computational location privacy

This is a literature survey of computational location privacy, meaning computation-based privacy mechanisms that treat location data as geometric information. This definition includes privacy-preserving algorithms like anonymity and obfuscation as well as privacy-breaking algorithms that exploit the geometric nature of the data. The survey omits non-computational techniques like manually inspecting geotagged photos, and it omits techniques like encryption or access control that treat location data as general symbols. The paper reviews studies of peoples’ attitudes about location privacy, computational threats on leaked location data, and computational countermeasures for mitigating these threats.

Accuracy characterization for metropolitan-scale Wi-Fi localization

Location systems have long been identified as an important component of emerging mobile applications. Most research on location systems has focused on precise location in indoor environments. However, many location applications (for example, location-aware web search) become interesting only when the underlying location system is available ubiquitously and is not limited to a single office environment. Unfortunately, the installation and calibration overhead involved for most of the existing research systems is too prohibitive to imagine deploying them across, say, an entire city. In this work, we evaluate the feasibility of building a wide-area 802.11 Wi-Fi-based positioning system. We compare a suite of wireless-radio-based positioning algorithms to understand how they can be adapted for such ubiquitous deployment with minimal calibration. In particular, we study the impact of this limited calibration on the accuracy of the positioning algorithms. Our experiments show that we can estimate a users position with a median positioning error of 13-40 meters (depending upon the characteristics of the environment). Although this accuracy is lower than existing positioning systems, it requires substantially lower calibration overhead and provides easy deployment and coverage across large metropolitan areas.

Predestination: inferring destinations from partial trajectories

We describe a method called Predestination that uses a history of a drivers destinations, along with data about driving behaviors, to predict where a driver is going as a trip progresses. Driving behaviors include types of destinations, driving efficiency, and trip times. Beyond considering previously visited destinations, Predestination leverages an open-world modeling methodology that considers the likelihood of users visiting previously unobserved locations based on trends in the data and on the background properties of locations. This allows our algorithm to smoothly transition between “out of the box” with no training data to more fully trained with increasing numbers of observations. Multiple components of the analysis are fused via Bayesian inference to produce a probabilistic map of destinations. Our algorithm was trained and tested on hold-out data drawn from a database of GPS driving data gathered from 169 different subjects who drove 7,335 different trips.

Location-aware computing comes of age

At the core of invisible computing is context awareness, the concept of sensing and reacting to dynamic environments and activities. Location is a crucial component of context, and much research in the past decade has focused on location-sensing technologies, location-aware application support, and location-based applications. With numerous factors driving deployment of sensing technologies, location-aware computing may soon become a part of everyday life.

Hidden Markov map matching through noise and sparseness

The problem of matching measured latitude/longitude points to roads is becoming increasingly important. This paper describes a novel, principled map matching algorithm that uses a Hidden Markov Model (HMM) to find the most likely road route represented by a time-stamped sequence of latitude/longitude pairs. The HMM elegantly accounts for measurement noise and the layout of the road network. We test our algorithm on ground truth data collected from a GPS receiver in a vehicle. Our test shows how the algorithm breaks down as the sampling rate of the GPS is reduced. We also test the effect of increasing amounts of additional measurement noise in order to assess how well our algorithm could deal with the inaccuracies of other location measurement systems, such as those based on WiFi and cell tower multilateration. We provide our GPS data and road network representation as a standard test set for other researchers to use in their map matching work.

PreHeat: controlling home heating using occupancy prediction

Home heating is a major factor in worldwide energy use. Our system, PreHeat, aims to more efficiently heat homes by using occupancy sensing and occupancy prediction to automatically control home heating. We deployed PreHeat in five homes, three in the US and two in the UK. In UK homes, we controlled heating on a per-room basis to enable further energy savings. We compared PreHeats prediction algorithm with a static program over an average 61 days per house, alternating days between these conditions, and measuring actual gas consumption and occupancy. In UK homes PreHeat both saved gas and reduced MissTime (the time that the house was occupied but not warm). In US homes, PreHeat decreased MissTime by a factor of 6-12, while consuming a similar amount of gas. In summary, PreHeat enables more efficient heating while removing the need for users to program thermostat schedules.

User-Generated Content

Pervasive user-generated content takes the traditional idea of user-generated content and expands it off the desktop into our everyday world. The six articles in this special issue give innovative examples of gathering and using such content.