Image Sensing-Based In-Building Human Demand Estimation for Installation of Automated External Defibrillators

The installation of automated external defibrillators (AEDs) in public places is encouraged, and the determination of the installation locations could be formed into mathematical programming models. In previous studies, data and parameters for those models were generated experimentally or manually. To estimate the parameters in practice for better reliability of the model outcomes, this research aims to design a procedure for the estimation of the real human demand distribution through automatic approaches. Inputs of the proposed work include the image sequences taken inside the building and the network describing the in-building geometry. Images are analyzed via computer vision methods for detection and tracking of people, and also augmented with the extra dimension of image depth estimation through a deep convolutional neural network. The key indicator to project the image back to the real space is identified by the correspondence between the location of humans appear in the image and the location of intersections of camera field of view with the network. Finally, the spatial-temporal human count is recorded to be the output and could serve as input parameter for related AED installation problems. The process is validated in image sequences taken at a corridor in a school building; the performance of each step and the influence of each step is discussed. In conclusion, our research provides a more automatic and robust approach to obtain the real spatial-temporal human distribution in public buildings.