Understanding Crowd Collectivity: A Meta-Tracking Approach

Understanding pedestrian dynamics in crowded scenes is an important problem. Given highly fragmented trajectories as input, we present a novel, fully unsupervised approach to automatically infer the semantic regions in a scene. Once the semantic regions are learned, given a tracklet of a person, our model predicts the pedestrian’s starting point and destination. The method is comprised of three steps. First, the spatial domain of the scene is quantized into hexagons and a 2D orientation distribution function (ODF) is learned for each hexagon. A Time Homogenous Markov Chain Meta-tracking method is used to automatically find the sources and sinks and later find the dominant paths in the scene. In the last step, using a 3-term based trajectory clustering method, we predict the source and sink for each pedestrian. Furthermore, we introduce a 2-step trajectory reconstruction method to infer the future behavior of each individual in the scene. Qualitative and quantitative experiments on a video surveillance dataset from New York Grand Central Station demonstrate the effectiveness of our method both in finding the semantic regions and grouping of fragmented tracklets.