Understanding Collective Crowd Behavior: A Computer Vision Approach

Recently I publish a research paper  at IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2012, to analyze the collective crowd behavior in New York Grand Central Station. Here is the paper and project page.

Grand Central Station

Generally speaking, the objective of this project is to learn the collective crowd behavior patterns from the real video of New York Grand Central Station. And the learned collective behavior patterns are used in a lot of important applications, such as crowd simulation, collective behavior classification, and abnormality detection.

Applications

Though there are quite a lot of pedestrian walking in the station at one time(~400 population) which form a variety of collective crowd behaviors, one key fact is that instead of randomly moving, majority of these pedestrian have clear belief of the destination to reach in mind, i.e., entering from one entry and walking to one exit in other side of the station. Thus, the overall behavior of one pedestrian in the station will be largely influenced by his belief of starting point and destination, along with two other properties： his preference of movement dynamics and timing of emerging (the frequency of entering the scene from the starting point).

Three typical pedestrian-agents and their three properties.

Following this intuitive analysis, from agent-based modeling of the crowd in station, every pedestrian is driven by one type of agents with three properties: the belief of starting point and destination, movement dynamics, and the timing of emerging. And the whole crowd is modeled as a mixture of pedestrian-agents with different three properties.

For the computational modeling of the pedestrian-agents, please refer to project page and paper for detailed information. Welcome to contact me if you have any questions or suggestions. The original video of the train station and the trajectories used in my paper could be downloaded at here.