perceiving motion transitions in pedestrian crowds qin gu, university of houston chang yun,...
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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
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
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
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
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
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)
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
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
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
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
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.