People Tracking

Estimating the location of people using a network of sensors placed throughout an environment is a fundamental challenge in smart environments and ubiquitous computing. Id-sensors such as infrared badges provide explicit object identity information but coarse location information while anonymous sensors such as laser range-finders provide accurate location information only. Tracking using both sensor types simultaneously is an open research challenge. We present a novel approach to tracking multiple objects that combines the accuracy benefits of anonymous sensors and the identification certainty of id-sensors. Rao-Blackwellised particle filters are used to estimate object locations. Each particle represents the association history between Kalman filtered object tracks and observations. After using only anonymous sensors until id estimates are certain enough, id assignments are sampled as well resulting in a fully Rao-Blackwellised particle filter over both object tracks and id assignments. Our approach was implemented and tested successfully using data collected in an indoor environment.