Abstract
The development of novel techniques for social modeling in the context of surveillance applications has significantly reduced manual processing of large and continuous video data. These techniques for social modeling widely cover crowd motion analysis since the impact of social modeling on crowd is significant. However, existing crowd motion analysis methods face a number of problems including limited availability of crowd data representing a specific behavior and weaknesses of proposed models to explore the underlying patterns of crowd behavior. To cope with these problems, we propose a novel method based on energy modeling and social interaction of individual particles in crowd to detect unusual behavior. Our method describes collective dissipative interactions among particles in a crowd scene. We reveal the changing patterns about the crowd behavior states, to support the conversion between different social behaviors during evolution. To further improve the performance of our method, virtual reality can be considered to consolidate the acquisition of data associated with a particular behavior. Therefore, we provide theoretical background of immersive implication considering virtual reality that can expose individuals to virtual crowds and acquire useful data on human motion and behaviors in crowds. The experimental evaluation of our energy and social interaction driven method shows convincing results.
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Ullah, H., Khan, S.D., Ullah, M., Cheikh, F.A. (2021). Social Modeling Meets Virtual Reality: An Immersive Implication. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12664. Springer, Cham. https://doi.org/10.1007/978-3-030-68799-1_10
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