Crowd Model

Crowd modeling aims to simulate and understand the collective behavior of groups of people, with applications ranging from urban planning and public safety to virtual reality and video analysis. Current research focuses on improving model accuracy and efficiency through techniques like incorporating visual information (e.g., using temporal convolutional networks), handling noisy or incomplete data from crowdsourced annotations (e.g., via label selection layers and adaptive filtering), and calibrating models using gradient-based optimization methods. These advancements enhance the realism and applicability of crowd models, leading to more accurate predictions of crowd dynamics and improved decision-making in various real-world scenarios.