Anomaly Detection Using Hierarchical Temporal Memory (HTM) in Crowd Management

Effective crowd management during events helps to avoid overcrowding that could lead to serious incidents and fatalities. Such application domain generates spatial and temporal resolution that demands diverse sophisticated mechanisms to measure, extract and process the data to produce a meaningful abstraction. Crowd management includes modelling the movements of a crowd to project effective mechanisms that support quick emersion from a dangerous and fatal situation. Internet of Things (IoT) technologies, machine learning techniques and communication methods can be used to sense the crowd density and offer early detection of such events or even better prediction of potential accidents to inform the management authorities. Different machine learning methods have been applied for crowd management; however, the rapid advancement in deep hierarchal models that learns from a continuous stream of data has not been fully investigated in this context e.g. Hierarchical Temporal Memory (HTM) has shown good potential for application domains that require online learning and modelling temporal information. In this paper, we propose a new HTM framework for crowd management. The proposed framework aims to detect anomalies in crowd movements and to predict potential overcrowding.