Informative Scene Decomposition for Crowd Analysis, Comparison and Simulation Guidance

摘要

Crowd simulation is a central topic in several fields including graphics. Toachieve high-fidelity simulations, data has been increasingly relied upon foranalysis and simulation guidance. However, the information in real-worlddata is often noisy, mixed and unstructured, making it difficult for effectiveanalysis, therefore has not been fully utilized. With the fast-growing volumeof crowd data, such a bottleneck needs to be addressed. In this paper, wepropose a new framework which comprehensively tackles this problem.It centers at an unsupervised method for analysis. The method takes asinput raw and noisy data with highly mixed multi-dimensional (space, timeand dynamics) information, and automatically structure it by learning thecorrelations among these dimensions. The dimensions together with theircorrelations fully describe the scene semantics which consists of recurringactivity patterns in a scene, manifested as space flows with temporal and dynamicsprofiles. The effectiveness and robustness of the analysis have beentested on datasets with great variations in volume, duration, environment and crowd dynamics. Based on the analysis, new methods for data visualization,simulation evaluation and simulation guidance are also proposed.Together, our framework establishes a highly automated pipeline from rawdata to crowd analysis, comparison and simulation guidance. Extensiveexperiments and evaluations have been conducted to show the flexibility,versatility and intuitiveness of our framework.