CLUST : simulating realistic crowd behaviour by mining pattern from crowd videos
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 a...
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sg-ntu-dr.10356-832712020-03-07T11:48:55Z CLUST : simulating realistic crowd behaviour by mining pattern from crowd videos Zhao, Mingbi Cai, Wentong Turner, S. J. School of Computer Science and Engineering Behavioural Animation Animation Engineering::Computer science and engineering 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. Accepted version 2019-10-03T05:10:18Z 2019-12-06T15:18:54Z 2019-10-03T05:10:18Z 2019-12-06T15:18:54Z 2017 Journal 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. doi:10.1111/cgf.13259 0167-7055 https://hdl.handle.net/10356/83271 http://hdl.handle.net/10220/50079 10.1111/cgf.13259 en Computer Graphics Forum 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. 17 p. application/pdf |
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Behavioural Animation Animation Engineering::Computer science and engineering Zhao, Mingbi Cai, Wentong Turner, S. J. CLUST : simulating realistic crowd behaviour by mining pattern from crowd videos |
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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. |
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School of Computer Science and Engineering |
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School of Computer Science and Engineering Zhao, Mingbi Cai, Wentong Turner, S. J. |
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Article |
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Zhao, Mingbi Cai, Wentong Turner, S. J. |
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Zhao, Mingbi |
title |
CLUST : simulating realistic crowd behaviour by mining pattern from crowd videos |
title_short |
CLUST : simulating realistic crowd behaviour by mining pattern from crowd videos |
title_full |
CLUST : simulating realistic crowd behaviour by mining pattern from crowd videos |
title_fullStr |
CLUST : simulating realistic crowd behaviour by mining pattern from crowd videos |
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CLUST : simulating realistic crowd behaviour by mining pattern from crowd videos |
title_sort |
clust : simulating realistic crowd behaviour by mining pattern from crowd videos |
publishDate |
2019 |
url |
https://hdl.handle.net/10356/83271 http://hdl.handle.net/10220/50079 |
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1681037704888844288 |