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Perceiving Motion Transitions in Pedestrian Crowds Qin Gu, University of Houston Chang Yun, University of Houston Zhigang Deng, University of Houston Virtual Reality Software and Technology (VRST) 2010

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Introduction UH CGIM Lab Walking motions of real pedestrians vary in both spatial and temporal domains. However, computer-generated pedestrians typically repeat the same walking pattern all the time. Robotic crowd Real crowd

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Related Work Improving crowd motion variety given a set of walking motion patterns: 1.Randomly select motions 2.Select motions based on examples [LCHL07], [LCL07], [LFCC09] 3.Select motions via heuristic rules [PAB07], [YT07], [GD10], UH CGIM Lab [LFCC09] Fitting Behaviors to Pedestrian Simulations, SCA 09

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Motivation 1.Interpolating motion patterns introduce unrealistic motion transitions. 2.Most transition optimizations for single character are computation consuming. [RGBC96] [KGP02] Our objective how macro crowd features make an illusion that the animation quality of each character in the crowd is visually improved without utilizing sophisticated optimization techniques. UH CGIM Lab

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Experiment Specifications HiDAC model [PAB 07]. Strategy view & FPS view 36 student participants 38 trials with 20 seconds of each Simple interpolation Uniform motion transition rate

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Crowd Density Effect Density: 8 Density: 64 Strategy viewFPS view

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Crowd Density Effect (2) Two-way analysis of variance was used to evaluate the average transition frequencies rated by the participants. (4 – 64 average density) Main effects: - Density of the crowd (F = 12.89, p < 0.017) - Viewpoint (F = 32.91, p < 0.001) Interaction: (F = 15.76, p < 0.018)

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Appearance Variety Effect UH CGIM Lab 1 texture 16 textures Strategy viewFPS view

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Appearance Variety Effect (2) UH CGIM Lab Two-way analysis of variance was used to evaluate the average transition frequencies rated by the participants. (1 – 16 textures) Main effects: - Appearance number (F = 17.72, p < 0.014) - Viewpoint (F = 23.13, p < 0.008) Interaction: no evident interaction

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Motion Variety Effect UH CGIM Lab Strategy viewFPS view 2 Motions 10 Motions

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Motion Variety Effect (2) UH CGIM Lab Two-way analysis of variance was used to evaluate the average transition frequencies rated by the participants. (2 – 10 motions) Main effects: - Motion number (F = 17.72, p < 0.014) - Viewpoint (F = 37.76, p < 0.006) Interaction: no evident interaction

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Group Interaction Effect UH CGIM Lab advection flocking chase random

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Group Interaction Effect (2) UH CGIM Lab Two-way analysis of variance was used to evaluate the average transition frequencies rated by the participants. (4 interactions) Main effects: - Motion number (F = 44.56, p < 0.004) - Viewpoint (F = 14.97, p < 0.012) Interaction: not available

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Summary A series of psychophysical experiments to investigate the influences of different viewpoints, crowd densities, appearance variations, motion variations, and collective group interactions. - Strategy viewpoint is more effective to hide motion transitions - Increasing the density of agent numbers helps to hide motion transitions. - Adding agent appearances does not lead to better perception of motion transitions in a crowd. - Increasing the number of motion candidates makes motion transitions easier to be detected - Collective behaviors or sub-group interactions can effectively decrease the negative impact of motion transitions. UH CGIM Lab

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Future work UH CGIM Lab Investigate the interactions among density, appearance variety and motion variety. Perform experiments on off-line crowds. Probe the transition perceptions on other types of crowd motions, such as running, talking, and fighting.

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Thank you! Presenter: Qin Gu UH CGIM Lab Project page: NSF IIS & Texas NHARP

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