使用邻近图的人群中的自动车道检测
Automated Lane Detection in Crowds using Proximity Graphs
摘要：
研究人群的行为对于理解和预测公共场所的人际互动至关重要。研究表明，在一定条件下，一大群人可以形成集体行为模式：个体之间的局部互动导致全球运动模式。为了检测人群中的这些模式，我们假设每个人都携带一个身体上的设备，该设备充当本地接近传感器，例如，智能手机或蓝牙徽章，并将人群的纹理表示为邻近图。我们的目标是从这些邻近图中提取关于人群的信息。在这项工作中，我们专注于一种特殊类型的模式：车道形成。我们提出了一个正式的车道定义，提出了一个简单的概率模型，模拟车道移动通过一个固定的人群，并提出了一个自动车道检测方法。我们的初步结果表明，我们的方法是能够检测不同形状和大小的车道。我们将我们的工作视为使用邻近图进行丰富模式识别的第一步。
英文摘要：
Studying the behavior of crowds is vital for understanding and predicting human interactions in public areas. Research has shown that, under certain conditions, large groups of people can form collective behavior patterns: local interactions between individuals results in global movements patterns. To detect these patterns in a crowd, we assume each person is carrying an on-body device that acts a local proximity sensor, e.g., smartphone or bluetooth badge, and represent the texture of the crowd as a proximity graph. Our goal is extract information about crowds from these proximity graphs. In this work, we focus on one particular type of pattern: lane formation. We present a formal definition of a lane, proposed a simple probabilistic model that simulates lanes moving through a stationary crowd, and present an automated lane-detection method. Our preliminary results show that our method is able to detect lanes of different shapes and sizes. We see our work as an initial step towards rich pattern recognition using proximity graphs.
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