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

Density: 8

Density: 64

Strategy view FPS view

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

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1 texture

16 textures

Strategy view FPS view

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

UH CGIM Lab

Strategy view FPS view

2 Motions

10 Motions

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

UH CGIM Labadvectionflocking

chaserandom

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.

Thank you!

Presenter: Qin Gu

UH CGIM Lab

Project page: http://graphics.cs.uh.edu/projects/CrowdTransitionPerception/

NSF IIS-0914965 & Texas NHARP 003652-0058-2007