multiple sensor integration for indoor surveillance
Multiple Sensor Indoor Surveillance (MSIS) is a research project at Accenture Technology Labs aimed at exploring a variety of redundant sensors in a networked environment where each sensor is giving noisy information and the goal is to coherently reason about some aspect of the environment. We describe the objectives of the project, the problems it was designed to solve and some recent results. The environment includes 32 web cameras, an infrared badge ID system, a PTZ camera, and a fingerprint reader. We discuss two concrete problems that we have tackled in this project: (1) Visualizing events detected by 32 cameras during 24 hours, and (2) Localizing people using fusion of multiple streams of noisy sensory data with the contextual and domain knowledge that is provided by both the physical constraints imposed by the local environment and by the people that are involved in the surveillance tasks. We use Self-Organizing Maps to approach the first problem and suggest a Bayesian framework for the second one. The experimental data are provided and discussed.
indoor surveillance visualization self-organizing maps bayesian inference unsupervised learning rare event detection people localization multi-camera surveillance