A framework for video-driven crowd synthesis.

We present a framework for video-driven crowd synthesis. The proposed framework employs motion analysis techniques to extract inter-frame motion vectors from the exemplar crowd videos. Motion vectors collected over the duration of the video are processed to compute global motion paths. These paths encode the dominant motions observed during the course of the video. These paths are then fed into a behavior-based crowd simulation framework, which is responsible for synthesizing crowd animations that respect the motion patterns observed in the video. Our system synthesizes 3D virtual crowds by animating virtual humans along the trajectories returned by the crowd simulation framework. We also propose a new metric for comparing the \visual similarity" between the synthesized crowd and exemplar crowd. We demonstrate the proposed approach on crowd videos collected under di fferent settings and the initial results appear promising.

Name thesis:degree_name

Master of Science (MSc)

Discipline thesis:degree_discipline

Nuclear Engineering

Grantor

University of Ontario Institute of Technology

Year dc:date.issued

2014

Author dc:creator

• Stadler, Jordan J.

Advisor dc:contributor.advisor

• Qureshi, Faisal Z.

• Framework
• Data-driven
• Crowd analysis
• Crowd synthesis

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/10155/469

OAI identifier oai:identifier

oai:ontariotechu.scholaris.ca:10155/469