Unravel: An Anomalistic Crowd Investigation Framework Using Social Airborne Sensing

In the domain of smart city applications, anomalistic crowd investigation in public places is a crucial task for the efficient management of unforeseen crowds. Many of the current approaches for crowd monitoring utilize physical sensor-based tracking (e.g., cellular signals, cameras, RFID beacons) to detect abrupt crowds. Unfortunately, these solutions are highly application-specific and are able to only detect the mere presence of crowds without determining their causes that may reveal the actual story behind gatherings that occur suddenly. In this paper, we explore the opportunity to meld two complementary sensing paradigms: social sensing and unmanned aerial vehicle (UAV)-based physical sensing to scrutinize abrupt crowd events occurring in public places. However, two technical challenges exist in developing our solution: i) handling the data sparsity across social sensing platforms and within video footage from UAVs; and ii) the accurate inference of the cause of crowd events from the collected data. To address these challenges, we develop Unravel, an intelligent social airborne sensing (SAS) framework that leverages interdisciplinary techniques from natural language processing (NLP), object recognition, and estimation theory for a comprehensive anomalistic crowd investigation. The evaluation results with a real-world anomalous crowd instance dataset show that the Unravel framework outperforms the state-of-the-art baselines in accurately validating the existence of anomalistic crowd instances in public areas and deriving their causes.