Model-Based Real-Time Estimation of Building Occupancy During Emergency Egress

Summary This paper provides a viable and practical solution to the challenge of real-time estimation of the number of people in areas of a building, during an emergency egress situation. Such estimates would be extremely valuable to first responders to aid in egress management, search-and-rescue, and other emergency response tactics. The approach of this paper uses an extended Kalman filter, which combines sensor readings and a dynamic stochastic model of people movement. The approach is demonstrated using two types of sensors: video with real-time signal processing to detect number of people moving in each direction across a threshold such as an entrance/exit, and passive infra-red motion sensors that detect people occupancy within its field of view. The people movement model uses the key idea that each room has a “high-density” and “low-density” area, where high-density corresponds to a queue of people at a bottleneck exit doorway, and low-density represents unconstrained flow of people. Another key feature of the approach is that constraints on occupancy levels and people flow rates are used to improve the estimation accuracy. The approach is tested using a stochastic discrete-time simulation model of a 1500 square meter office building with occupancy up to 100 people, having a video camera at each of the three exits, and motion sensors in each of the 42 office rooms. The simulation includes stochastic models of video sensors having a probability of detection of 98%, and motion sensors with probability of detection of 80%. Averaged over 100 simulation runs and averaged over the evacuation time, the sensor-only approach produced a mean estimation error per room of 0.35 people, the Kalman filter with cameras only had a mean error of 0.14 people, and the Kalman filter with all sensors produced a mean error of 0.09 people. These results show that an effective combination of models and sensors greatly improves estimation accuracy compared to the state-of-the-art practice of using sensors only.