Measuring Crowd Collectiveness

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.

Reference:

• 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)