Automated Lane Detection in Crowds using Proximity Graphs

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 paˆerns: local interactions between individuals results in global movements paŠerns. To detect these paŠerns 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 paŠern: lane formation. We present a formal de€nition 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 di‚erent shapes and sizes. We see our work as an initial step towards rich paŠern recognition using proximity graphs.