Crowd Motion

Crowd motion research focuses on understanding and predicting the complex movements of individuals within dense groups, aiming to improve safety and efficiency in various applications like robotics and autonomous driving. Current research employs advanced deep learning models, such as transformer networks and graph-based methods, to capture both pairwise and group-level interactions within crowds, often incorporating multi-scale analysis to account for varying levels of collective behavior. These efforts are driven by the need for robust and accurate crowd motion prediction for applications requiring safe and efficient navigation in crowded environments, as well as for detecting abnormal crowd behaviors that could indicate potential hazards. The development of efficient and generalizable models remains a key challenge, with ongoing work focusing on improving model accuracy and reducing computational demands for real-time applications.