Date Published: April 11, 2019
Publisher: Public Library of Science
Author(s): Haoran Zhang, Xuan Song, Xiaoya Song, Dou Huang, Ning Xu, Ryosuke Shibasaki, Yongtu Liang, Olalekan Uthman.
Ex-ante online risk assessment for building emergency evacuation is essential to protect human life and property. Current risk assessment methods are limited by the tradeoff between accuracy and efficiency. In this paper, we propose an online method that overcomes this tradeoff based on multimedia data (e.g. videos data from surveillance cameras) and deep learning. The method consists of two parts. The first estimates the evacuee position as input for training the assessment model to then perform risk assessment in real scenarios. The second considers a social force model based on the evacuation simulation for the output of training model. We verify the proposed method in simulation and real scenarios. Model sensitivity analyses and large-scale tests demonstrate the usability and superiority of the proposed method. By the method, the computation time of risk assessment could be decreased from 10 minutes (by traditional simulation method) to 2.18 s.
Building emergency evacuation is an indispensable process to protect human life and property under the occurrence of events such as fires, earthquakes, and terrorist attacks [1,2]. Statistics show that emergencies causing numerous casualties mostly occur by either the lack of reliable evacuation facilities or mismanagement for safe and timely evacuation, especially in crowded public places such as shopping malls, stadiums, theatres, and other entertainment venues. Employing more evacuation guiders and facilities seems the direct way to avoid the casualties but also clumsy and high-cost. If security administrators can be informed about the real-time potential risk, they can take the relatively suitable ex-ante measures (adding more guiders and facilities) and avoid the overreaction or the negligence hence to save the cost and improve safety. Therefore, ex-ante online risk assessment for building emergency evacuation can be greatly useful to guide decision-making for extreme event prevention, mitigation, preparedness, and response [3,4].
The current demand for smart and safe city development makes emergency evacuation one of the hottest topics in fields including risk management, public health, and urban planning. In fact, over 22 thousand articles report emerging applications, discussions, and methodologies related to this topic within the last 5 years[7,8]. Most studies have focused on probabilistic risk models, simulations , evacuation drills , and socio-psychological aspects of documented disasters . However, the design of online risk assessment systems has been scarcely addressed. The existing approaches can be divided into three types, namely, analyses based on building the structure, evacuees distribution, and data.
The model to estimate evacuee status aims to determine the number and position of evacuees within a building. Although surveillance videos can provide plenty of visual information, additional intensive processing is required. There are several image-based human detection methods currently available, such as Faster R-CNN  and Mask R-CNN , which are able to quickly determine the number of people by using neural networks. In fact, object detection is a complex and important field of computer vision and pattern recognition [25–27]. Therefore, we limit ourselves to employ the available human detection method in the proposed method for risk assessment and do not discuss this aspect in detail.
The proposed risk assessment method is composed of a social force model to compute the output of a training model, which maps the evacuees’ state to limit the time for evacuation.
To evaluate the proposed method considering its sensitivity with respect to both the number of evacuees and their positions during building emergency evacuation, we implemented a simulation scenario that consists of a single-floor building with eight offices and one exit, as shown in Fig 3(a). Sorting by the mean distance between the centre of each room and exit, four kinds of room are numbered.
In this paper, we propose an ex-ante online risk assessment method for building emergency evacuation. The general framework of the proposed method is detailed, and it allows to provide fast response and blind spot detection. By analyzing the model sensitivity and its performance in a real scenario, we demonstrate the usability and performance of the proposed method. In addition, the results from the real scenario show that the proposed method can perform risk assessment in complex settings.