Segmentation And Visualization Of Crowd Flows In Videos Using Hybrid Force Model

Understanding crowd phenomena is a challenging task. It can help to monitor crowds to prevent unwanted incidents. Crowd flow is one of the most important phenomena that describes the motion of people in crowded scenarios. Crowd flow analysis is popular among the computer vision researchers as this can be used to describe the behavior of the crowd. In this paper, a hybrid model is proposed to understand the flows in densely crowded videos. The proposed method uses the Smooth Particle Hydrodynamics (SPH)-based method guided by the Langevin-based force model for the segmentation of linear as well as non-linear flows in crowd gatherings. SPH-based model identifies the coherent motion groups. Their behavior is then analyzed using the Langevin equation guided force model to segment dominant flows. The proposed method, based on the hybrid force model, has been evaluated on public video datasets. It has been observed that the proposed hybrid scheme is able to segment linear as well as non-linear flows with accuracy as high as 91.23%, which is 4-5% better than existing crowd flow segmentation algorithms. Also, our proposed method's execution time is better than the existing techniques.