Home > Uncategorized > Scene-Independent Group Profiling in Crowd

Scene-Independent Group Profiling in Crowd

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.shao_fig2

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]


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