Deep Learning for Crowd Anomaly Detection

Today, public areas across the globe are monitored by an increasing amount of surveillance cameras. This widespread usage has presented an ever-growing volume of data that cannot realistically be examined in real-time. Therefore, efforts to understand crowd dynamics have brought light to automatic systems for the detection of anomalies in crowds. This thesis explores the methods used across literature for this purpose, with a focus on those fusing dense optical flow in a feature extraction stage to the crowd anomaly detection problem. To this extent, five different deep learning architectures are trained using optical flow maps estimated by three deep learning-based techniques. More specifically, a 2D convolutional network, a 3D convolutional network, and LSTM-based convolutional recurrent network, a pre-trained variant of the latter, and a ConvLSTM-based autoencoder is trained using both regular frames and optical flow maps estimated by LiteFlowNet3, RAFT, and GMA on the UCSD Pedestrian 1 dataset. The experimental results have shown that while prone to overfitting, the use of optical flow maps may improve the performance of supervised spatio-temporal architectures

A roadmap for the future of crowd safety research and practice: Introducing the Swiss Cheese Model of Crowd Safety and the imperative of a Vision Zero target

Crowds can be subject to intrinsic and extrinsic sources of risk, and previous records have shown that, in the absence of adequate safety measures, these sources of risk can jeopardise human lives. To mitigate these risks, we propose that implementation of multiple layers of safety measures for crowds—what we label The Swiss Cheese Model of Crowd Safety—should become the norm for crowd safety practice. Such system incorporates a multitude of safety protection layers including regulations and policymaking, planning and risk assessment, operational control, community preparedness, and incident response. The underlying premise of such model is that when one (or multiple) layer(s) of safety protection fail(s), the other layer(s) can still prevent an accident. In practice, such model requires a more effective implementation of technology, which can enable provision of real-time data, improved communication and coordination, and efficient incident response. Moreover, implementation of this model necessitates more attention to the overlooked role of public education, awareness raising, and promoting crowd safety culture at broad community levels, as one of last lines of defence against catastrophic outcomes for crowds. Widespread safety culture and awareness has the potential to empower individuals with the knowledge and skills that can prevent such outcomes or mitigate their impacts, when all other (exogenous) layers of protection (such as planning and operational control) fail. This requires safety campaigns and development of widespread educational programs. We conclude that, there is no panacea solution to the crowd safety problem, but a holistic multi-layered safety system that utilises active participation of all potential stakeholders can significantly reduce the likelihood of disastrous accidents. At a global level, we need to target a Vision Zero of Crowd Safety, i.e., set a global initiative of bringing deaths and severe injuries in crowded spaces to zero by a set year