用于人群分析、比较和仿真引导的信息场景分解
Informative Scene Decomposition for Crowd Analysis, Comparison and Simulation Guidance
摘要：
人群模拟是包括图形学在内的多个领域的中心课题。为了实现高保真模拟，越来越多地依赖数据进行分析和模拟指导。然而，现实世界中的数据信息往往是嘈杂的、混杂的、非结构化的，难以进行有效的分析，因此没有得到充分的利用。随着人群数据量的快速增长，需要解决这样的瓶颈。在本文中，我们提出了一个新的框架，全面解决这个问题。它的核心是一种无监督的分析方法。该方法将具有高度混合多维（空间，时间和动态）信息的原始和噪声数据作为输入，并通过学习这些维度之间的相关性来自动构建它。这些维度及其相关性充分描述了场景语义，场景语义由场景中反复出现的活动模式组成，表现为具有时间和动态轮廓的空间流。分析的有效性和鲁棒性已经在体积、持续时间、环境和人群动态变化很大的数据集上进行了测试。在此基础上，提出了数据可视化、仿真评估和仿真指导的新方法。总之，我们的框架建立了一个从原始数据到人群分析、比较和模拟指导的高度自动化的管道。广泛的实验和评估已经进行了展示我们的框架的灵活性，通用性和直观性。
英文摘要：
Crowd simulation is a central topic in several fields including graphics. To achieve high-fidelity simulations, data has been increasingly relied upon for analysis and simulation guidance. However, the information in real-world data is often noisy, mixed and unstructured, making it difficult for effective analysis, therefore has not been fully utilized. With the fast-growing volume of crowd data, such a bottleneck needs to be addressed. In this paper, we propose a new framework which comprehensively tackles this problem. It centers at an unsupervised method for analysis. The method takes as input raw and noisy data with highly mixed multi-dimensional (space, time and dynamics) information, and automatically structure it by learning the correlations among these dimensions. The dimensions together with their correlations fully describe the scene semantics which consists of recurring activity patterns in a scene, manifested as space flows with temporal and dynamics profiles. The effectiveness and robustness of the analysis have been tested 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 raw data to crowd analysis, comparison and simulation guidance. Extensive experiments and evaluations have been conducted to show the flexibility, versatility and intuitiveness of our framework.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
