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We present a novel procedural framework to generate an arbitrary number oflabeled crowd videos (LCrowdV). The resulting crowd video datasets are used todesign accurate algorithms or training models for crowded scene understanding.Our overall approach is composed of two components: a procedural simulationframework for generating crowd movements and behaviors, and a proceduralrendering framework to generate different videos or images. Each video or imageis automatically labeled based on the environment, number of pedestrians,density, behavior, flow, lighting conditions, viewpoint, noise, etc.Furthermore, we can increase the realism by combining synthetically-generatedbehaviors with real-world background videos. We demonstrate the benefits ofLCrowdV over prior lableled crowd datasets by improving the accuracy ofpedestrian detection and crowd behavior classification algorithms. LCrowdVwould be released on the WWW.

LCrowdV: Generating Labeled Videos for Simulation-based Crowd Behavior Learning