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|Title:||CLUST : simulating realistic crowd behaviour by mining pattern from crowd videos||Authors:||Zhao, Mingbi
Turner, S. J.
Engineering::Computer science and engineering
|Issue Date:||2017||Source:||Zhao, M., Cai, W., & Turner, S. J. (2018). CLUST : simulating realistic crowd behaviour by mining pattern from crowd videos. Computer Graphics Forum, 37(1), 184-201. doi:10.1111/cgf.13259||Series/Report no.:||Computer Graphics Forum||Abstract:||In this paper, we present a data‐driven approach to simulate realistic locomotion of virtual pedestrians. We focus on simulating low‐level pedestrians' motion, where a pedestrian's motion is mainly affected by other pedestrians and static obstacles nearby, and the preferred velocities of agents (direction and speed) are obtained from higher level path planning models. Before the simulation, collision avoidance processes (i.e. examples) are extracted from videos to describe how pedestrians avoid collisions, which are then clustered using hierarchical clustering algorithm with a novel distance function to find similar patterns of pedestrians' collision avoidance behaviours. During the simulation, at each time step, the perceived state of each agent is classified into one cluster using a neural network trained before the simulation. A sequence of velocity vectors, representing the agent's future motion, is selected among the examples corresponding to the chosen cluster. The proposed CLUST model is trained and applied to different real‐world datasets to evaluate its generality and effectiveness both qualitatively and quantitatively. The simulation results demonstrate that the proposed model can generate realistic crowd behaviours with comparable computational cost.||URI:||https://hdl.handle.net/10356/83271
|ISSN:||0167-7055||DOI:||http://dx.doi.org/10.1111/cgf.13259||Rights:||This is the peer reviewed version of the following article: Zhao, M., Cai, W., & Turner, S. J. (2018). CLUST : simulating realistic crowd behaviour by mining pattern from crowd videos. Computer Graphics Forum, 37(1), 184-201, which has been published in final form at http://dx.doi.org/10.1111/cgf.13259. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||SCSE Journal Articles|
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