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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.

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Presentation on theme: "Perceiving Motion Transitions in Pedestrian Crowds Qin Gu, University of Houston Chang Yun, University of Houston Zhigang Deng, University of Houston Virtual."— Presentation transcript:

1 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

2 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

3 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

4 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

5 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

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

7 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)

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

9 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

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

11 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

12 Group Interaction Effect UH CGIM Lab advection flocking chase random

13 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

14 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

15 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.

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


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