Modeling Collective Crowd Behaviors in Video

Crowd behavior analysis is an interdisciplinary topic. Understanding the collective crowd behaviors is one of the fundamental problems both in social science and natural science. Research of crowd behavior analysis can lead to a lot of critical applications, such as intelligent video surveillance, crowd abnormal detection, and public facility optimization. In this thesis, we study the crowd behaviors in the real scene videos, propose computational frameworks and techniques to analyze these dynamic patterns of the crowd, and apply them for a lot of visual surveillance applications. Firstly we proposed Random Field Topic model for learning semantic regions of crowded scenes from highly fragmented trajectories. This model uses the Markov Random Field prior to capture the spatial and temporal dependency between tracklets and uses the source-sink prior to guide the learning of semantic regions. The learned semantic regions well capture the global structures of the scenes in long range with clear semantic interpretation. They are also able to separate different paths at fine scales with good accuracy. This work has been published in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2011 [70]. To further explore the behavioral origin of semantic regions in crowded scenes, we proposed Mixture model of Dynamic Pedestrian-Agents to learn the collective dynamics from video sequences in crowded scenes. The collective dynamics of pedestrians are modeled as linear dynamic systems to capture long range moving patterns. Through modeling the beliefs of pedestrians and