Home > Uncategorized > Stationary Groups in Crowd Situations

Stationary Groups in Crowd Situations

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.

Figure 1

Figure 1. Four major types of stationary group activities to be detected in our work. (a) People join a group from different directions at different time. When all people arrive, the whole group moves along the same destination. (b) A group of people enter the view together, stay for a period of time, and leave together. (c) After staying at a place for a while, people move to another location and become stationary again. (d) People in a group have their own activities, taking photos for example.

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.


Figure 2. The emergence and dispersal of stationary crowd groups will cause the dynamic variations of traffic patterns. Stationary groups are marked in red and main traffic patterns are marked in blue.


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.


Figure 3. Average stationary time distribution over 4 hours. Stationary groups tend to emerge and stay long around the information booth and in front of the ticketing windows.

All the above mentioned applications rely on one key technology of stationary time estimation. We propose a new method that estimates stationary time[1], 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.

Figure 1

Figure 4. Estimating a 3D stationary time map from a video sequence. Results from a few frames are shown. How long a pixel has been stationary up to each frame is encoded by the intensity level. Brighter pixels correspond to longer time.

[1] 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]

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