A review on collective motion written by Tamas Vicsek is finally published, which introduces the state-of-the-art research on collective motion. In my opinion it is the greatest review on collective motion so far for its thoroughness and wide coverage. Check it out at here. The arXiv preprint is at here.
Collective Motion, Tamás Vicsek, Anna Zafeirisa, Physics Reports, 2012
Besides, Prof. Vicsek is a leading researcher on collective motion and complex network. You could see his homepage for a lot of useful information on this exciting research direction.
A TEDx talk titled The Simple and the Complex given by Prof. Vicsek is here.
A new article published at Wired gives a very nice introduction on the state-of-the-art research of swarm and crowd. Enjoy!
A large group of Bigeye travellies at Cabo Pulmo National Park, Mexico, captured by Octavio Aburto.
Coherent motion is a universal phenomenon in nature and widely exists in many physical and biological systems. For example, the tornadoes, storms and atmospheric circulation are all caused by the coherent movements of physical particles in the atmosphere. Meanwhile, the collective behaviors of organisms such as schooling fishes and pedestrian crowd have long captured the interests of social and natural scientists. Here are examples of coherent motions in videos.
Detecting these coherent motion patterns in crowd is the first step to organize the low-level features into semantic clusters. It will benefit high-level tasks such as scene understanding and activity analysis.
Recently I proposed a simple coherent motion detection technique called Coherent Filtering. It is published in Proceedings of 12th European Conference on Computer Vision (ECCV 2012). It is a generic clustering algorithm for analyzing time-series signals.
In our formulation, the low-level features are the keypoint trajectories (short time-series) automatically extracted from crowd video. Here are examples of the keypoint trajectories extracted from the crowd videos.
Since the scenes in video are very crowded, there will be lots of dynamic noises and cluttered trajectories. Thus the purpose of the technique is to remove these noises and cluster keypoint trajectories into different coherent motion patterns. Here are some clustering results:
The mechanism behind our technique is that it is based on a prior discovered in the particle dynamics called Coherent Neighbor Invariance. The details can be found at the project page and technical paper.
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.
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.
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).
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.
Here is a new book on Agent-Based Models of Geographical Systems published by Springer 2012.
Agent-based model is a very powerful research tools for crowd behavior analysis. I went through this book, there are two chapters directly related to crowd behavior analysis:
- Chapter 18: Agent tools, techniques and methods for macro and microscopic simulation.
- Chapter 21: Applied Pedestrian Modeling.
Besides, in Chapter 12: The Integration of Agent-based Modeling and Geographical Information for Geospatial Simulation, it introduces and compares several kinds of Agent-based simulation open source toolkits.
Here I list :
- Swarm: developed by Center for the Study of Complex Systems at the University of Michigan.
- MASON (Multi Agent Simulation Of Neighbourhood): developed by the Evolutionary Computational Laboratory and the Centre for Social Complexity at George Mason University
- StarLogo: developed by Media Lab at MIT.