Tiltor, Stop Protests From Becoming Riots

February 4, 2014 Leave a comment

Here I would like to recommend a mobile APP system called Titor, which aims at stopping crowd protests from becoming riots. The following introduction article on Tiltor is written by the CEO and Co-Founder of Tiltor, Greg Millington.

Anyone who has ever had the pleasure of joining a protest, doing the mexican wave, or attending a flash mob knows that it doesn’t take long to snap into sync with the crowd. This is because formed crowds are self-organizing systems with many parts supporting a particular global behavior. An entrant to an existing crowd is swept into lock step without having to be explicitly told what to do. Since self-organizing systems are not centrally controlled, but rather each person plays a role in maintaining the global behavior, they can withstand damages and perturbations such as the loss of participants. This feature makes protesting crowds notoriously difficult to terminate and it is exactly what Tiltor attacks.

In this respect, Tiltor can be thought of as the “Anti-Flash Mob.” Instead of making people spontaneously form a crowd, we make an already formed crowd of people disperse.newtiltorHIW

Tiltor is designed to compromise the collective motivation of a protesting crowd. When a significant segment of a crowd has their allegiances flipped, it does more than only peel away that segment from the protest (which a self-organized system can survive). Rather, the “turned” population of protestors serves to disrupt the system. This has a much greater undermining effect on the crowd sync than if they had just quietly left the protest. By flipping the feedback loop within the crowd from positive (“do what you can to keep the protest going”) to negative (“do what you can to stop the protest”) the crowd behavior can be altered, or even extinguished.

Tiltor presents this non-violent means of crowd control to law enforcement authorities in the hopes that the increasingly deadly clashes between protesters and police can be avoided. Upcoming applications of TIltor include the Winter Olympics in Sochi and the World Cup in Brazil.

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Measuring Crowd Collectiveness

January 16, 2014 3 comments

As we know that crowds in nature have a variety of scales, shapes, and dynamics. To quantitatively analyze the dynamic properties of crowd, we need to have a general descriptor that could measure the level of collective motions in crowd.

The simple and naive measure is the average velocity of the whole crowd, but we found that this measure is sensitive to noise and the global shapes of the crowd movement. Like these crowds in the following Figure, if the crowd move globally in a C shape, the average velocity would be very small, but in fact the ‘collectiveness’ of the crowd is high.


In recent work of Zhou et al (CVPR2013 oral, TPAMI2014) , a new descriptor of crowd called Collectiveness is proposed. This descriptor utilizes the graph connectivity of individuals in the neighborhood to build a global indicator to measure the collective level of crowd motions. As shown below,  crowd movement could be accurately estimated and quantified into different dynamic categories.

crowd_img1Besides, there are a lot of applications based on this general descriptor, such as monitoring crowd dynamics in videos, detecting collective motions in time-series data, and generating collective map of scenes. Just check the TPAMI journal paper of this work.



  • Bolei Zhou, Xiaoou Tang, and Xiaogang Wang. “Measuring Crowd Collectiveness.” Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2013, oral paper)
  • Bolei Zhou, Xiaoou Tang, Hepeng Zhang, and Xiaogang Wang. “Measuring Crowd Collectiveness.” The IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI, regular paper)


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A Complete Review on Collective Motion

May 9, 2013 Leave a comment

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.

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The Power of Swarms Can Help Us Fight Cancer, Understand the Brain, and Predict the Future

March 19, 2013 Leave a comment

A new article published at Wired gives a very nice introduction on the state-of-the-art research of swarm and crowd. Enjoy!

The Power of Swarms Can Help Us Fight Cancer, Understand the Brain, and Predict the Future


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Breathtaking View of the Underwater

December 18, 2012 Leave a comment

A large group of Bigeye travellies at Cabo Pulmo National Park, Mexico, captured by Octavio Aburto.

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Coherent Filtering: Detecting Coherent Motions from Crowd Clutter

October 2, 2012 Leave a comment

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.

Figure 1: Coherent motions in nature.

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.

Figure 2. A) One frame of the crowd videos. B) The trajectories extracted from the videos. Colors of trajectories are randomly assigned.

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:

Figure 3. The coherent motion detection results by Coherent Filtering. Keypoints with the same color belong to the same coherent motion pattern.

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.

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Understanding Collective Crowd Behavior: A Computer Vision Approach

July 6, 2012 4 comments

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.


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.

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