Pedestrian Origin-Destination Estimation in Emergency Scenarios

A fundamental building block towards efficient emergency management is the information on pedestrian route choices in emergency scenarios. Origin-destination (O-D) matrix is usually used to represent the information on the volume of pedestrians travelling from a set of origin zones to a set of destination zones. Conventional pedestrian O-D estimation methods, such as surveys, smart card, Bluetooth, Wi-Fi or CCTV cameras, require active user involvement or favorable lighting conditions. Motivated by the wide applications of RGB-D cameras that can provide an ambient illumination-independent channel, in this paper, we propose an RGB-D based pedestrian O-D estimation framework, which can work properly in fires, or low lighting conditions. Firstly, we train a joint convolutional neural network (CNN) on three modalities, including RGB, depth and infrared images. Subsequently, for evaluating person re-identification algorithm and O-D estimation framework, we collect and annotate a new top view RGB-D dataset considering different levels of service and lighting conditions, which we will make publicly available. Our experiment results demonstrate that the fusion of multiple modalities improves the pedestrian detection and reidentification accuracy by 13.4% compared with using single RGB modality. Especially in low lighting conditions where RGB cameras perform poorly, we can still achieve an accuracy of 72.8% using depth cameras.