Self-organizing maps
Fusion of Multiple Tracking Algorithms for Robust People Tracking
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Multi-Camera Multi-Person Tracking for EasyLiving
VS '00 Proceedings of the Third IEEE International Workshop on Visual Surveillance (VS'2000)
An embedded HMM-based approach for face detection and recognition
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 06
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
A flexible framework for multisensor data fusion using data stream management technologies
Proceedings of the 2009 EDBT/ICDT Workshops
An access control and time management software solution using RFID
CompSysTech '09 Proceedings of the International Conference on Computer Systems and Technologies and Workshop for PhD Students in Computing
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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.