Understanding crowd flow movements

Understanding crowd flow movements

Problem being addressed

Crowd flow describes the elementary group behavior of crowds and understanding the dynamics behind these movements can help to identify various abnormalities in crowds. However​,​ developing a crowd model describing these flows is a challenging task.

Solution

A physics-based model that describes the movements in dense crowds. The crowd model is based on active Langevin equation where the motion points are assumed to be similar to active colloidal particles in fluids. The model is further augmented with computer-vision techniques to segment both linear and non-linear motion flows in a dense crowd. In the future, the proposed model can be augmented with machine-learning approaches for identifying and predicting abnormal regions in crowded scenes.

Advantages of this solution

The proposed method is able to segment the flow with lesser optical flow error and better accuracy in comparison to existing state-of-the-art methods.

Solution originally applied in these industries

sports

Sports Industry

Possible New Application of the Work

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

Infrastructure Industry

Modelling of crowd flows is commonly used in architecture and urban planning in order to create smart spaces that combine convenience and safety.

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Other Industry

Security: evacuation simulation is the area where understanding of the crowd flow is truly vital; accurate modelling of this process allows to take predictive measures instead of being reactive, which is crucial for ensuring security of the citizens.

Author of original research described in this blitzcard:

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Name of the author who conducted the original research that this blitzcard is based on.

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