Crowd Behaviour Prediction using Visual and Location Data in Super-Crowded Scenarios

Abstract

Predicting the future trajectory of a crowd is important for safety to prevent disasters such as stampedes or collisions. Extensive research has been conducted to explore trajectory prediction in typical crowd scenarios, where the majority of individuals can be easily identified. However, this study focuses on a more challenging scenario known as the super-crowd scene, wherein individuals within the crowd can only be annotated based on their heads. In this particular scenario, people’s re-identification process in tracking does not perform well due to a lack of clear image data. Our research proposes a clustering strategy to overcome people re-identification problems and predict the cluster crowd trajectory. Two-dimensional(2D) maps and multi-cameras will be used to capture full pictures of crowds in a location and extract the venue’s spatial data (see figure 1). The research methodology encompasses several key steps, including evaluating data extraction of the state-of-the-art methods, estimating crowd clusters, integrating 2D maps and multi-view fusion, and evaluating the proposed method on a dataset of multi-view videos collected in a real-world super-crowded scenario.