By Jing Shao
Groups are the primary entities that make up a crowd. Understanding group-level dynamics and properties is thus scientifically important and practically useful in a wide range of applications, especially for crowd understanding.
Socio-psychologists and biologists have extensively studied group dynamics as the primary processes that influence crowd behaviors. Group dynamics contain both intra- and inter- aspect: e.g. bacterial colonies were found to exhibit collective behavior to achieve a common goal, i.e. spreading of diseases; Conflict often occurs during competition of resources or goal incompatibility, either in fish schools or ant swarm.
In recent work of Shao et al (CVPR2014), a universal and fundamental set of group properties and corresponding scene-independent visual descriptors are proposed. This is made possible through learning a novel Collective Transition prior, which leads to a robust approach for group segregation in public spaces. From the prior, a set of visual descriptors are devised as shown below.
Understanding such properties provides critical mid-representation to crowd motion analysis, and could facilitate other high-level semantic analysis such as crowd scene understanding, crowd video classification, and crowd event retrieval. Both applications are scene-independent.
Jing Shao, Chen Change Loy, Xiaogang Wang. “Scene-Independent Group Profiling in Crowd.” Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2014) [PDF] [Abstract] [Bibtex] [Project page]
by Shuai Yi
With steady population growth and worldwide urbanization, more and more people gather in big cities and crowd situation is happening more and more often. Crowd analysis in video surveillance attracts lots of attention and has plenty of applications. Existing work focuses on detecting motion patterns of crowds and analyzing interactions among pedestrians during movement. On the other hand, stationary crowd group analysis has never been sufficiently studied although these groups can provide surprisingly rich information.
Stationary crowd group is playing an important role in crowd analysis. It is one of the most common and basic pattern in crowd situations. Groups that stay for a period of time are often worth attention, as most interesting and attractive activities happen on the persons staying in the scene for a relatively long time rather than those passing through the scene quickly.
First of all, we can detect different types of group activities and discover valuable information from these activities. Figure 1 shows four activities that are to be detected. They are group gathering, group stopping by, group relocating, and group deformation, respectively. From different group activities, we may infer underlying social relationship of group members. For some groups, group members are familiar with each other (e.g. friends waiting for each other, or a group of people having discussion), while some others are just unfamiliar people sharing the same goal (e.g. buying tickets together or waiting for the same train). Moreover, the emergence, dispersal, stationary duration, and status of stationary groups may incur great security interest and are necessary to be discovered.
Secondly, stationary groups will change traffic flow and will decrease traffic efficiency. Previous works mainly model the global motion pattern based on scene structures (e.g. entrances, exits, walls, and roads) and the interactions among individual moving pedestrians. However, study of shows that stationary groups have a greater impact on changing traffic patterns than mobile pedestrians in some situations. When pedestrians move around, they adjust speed but not direction to avoid collisions. Such self-organized behaviors keep traffic flow smooth. However, if pedestrians form stationary groups, they force others to change directions and transportation efficiency will be decreased a lot. As shown in Figure 2, the emergence and dispersal of stationary groups cause dynamic variation of crowd traffic patterns.It is thus of great interest to incorporate stationary groups into dynamic modeling of crowd systems. Moreover, stationary groups will lead to lower efficiency as pedestrians need to walk a longer way to bypass these groups, and special attention should be paid to this area.
Lastly, stationary groups can help us better understand scene structure. It is informative to investigate where stationary groups are likely to emerge and how long they tend to stay. An average stationary time map is shown in Figure 3. It can provide guidance for crowd management, as well as provision of facilities and support.
All the above mentioned applications rely on one key technology of stationary time estimation. We propose a new method that estimates stationary time, i.e., period that a foreground pixel exists in a local region allowing local movements. As shown in Figure 4, given a video sequence, our method produces a 3D stationary time map in the spatio-temporal space. It is an important step for further analysis on stationary crowds.
 Shuai Yi, Xiaogang Wang, Cewu Lu, and Jiaya Jia. “L0 Regularizes Stationary Time Estimation for Crowd Group Analysis.” Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2014). [Paper] [Spotlight] [Demo] [Poster] [Presentation] [Abstract] [Bibtex]
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.
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.
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.
Besides, 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)
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!