Video-based estimation of building occupancy during emergency egress

Providing real-time estimates of building occupancy to first responders during emergency events can help in search and rescue, and egress management. This paper addresses the estimation of occupancy in each zone of a building, where the building is spatially divided into non-overlapping zones that cover all areas of the building. Each zone contains video cameras located at each portal of the zone, where each camera has a signal processing algorithm that detects number of people passing through the portal in each direction. The technical approach of this paper is to develop a non-linear stochastic state-space model of people traffic during emergency egress, and apply the extended Kalman filter which uses the video signal processing outputs and the people traffic model. The approach is demonstrated on a 16,000 square-foot building that has typical occupancy of 100 people. The estimator is tested on data from an agent-based simulation, and on data from an actual fire alarm. The results show that better estimation accuracy is achieved compared to an estimation approach that uses only the video sensors.