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

arXiv: https://arxiv.org/abs/1606.08998 · HTML: https://mlsanity.com/paper/1606.08998/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jun 2016.

## Built on by (most cited first, 6 of 6)

- [A survey of synthetic data augmentation methods in computer vision](https://mlsanity.com/paper/2403.10075/) (2024)
- [Cross-View Cross-Scene Multi-View Crowd Counting](https://mlsanity.com/paper/2205.01551/) (2022)
- [DroNeRF: Real-time Multi-agent Drone Pose Optimization for Computing Neural Radiance Fields](https://mlsanity.com/paper/2303.04322/) (2023)
- [Attention-based Adversarial Appearance Learning of Augmented Pedestrians](https://mlsanity.com/paper/2107.02673/) (2021)
- [UAV-CROWD: Violent and non-violent crowd activity simulator from the perspective of UAV](https://mlsanity.com/paper/2208.06702/) (2022)
- [A Unified Simulation Framework for Visual and Behavioral Fidelity in Crowd Analysis](https://mlsanity.com/paper/2312.02613/) (2023)

## Abstract (arXiv)

We present a novel procedural framework to generate an arbitrary number of labeled crowd videos (LCrowdV). The resulting crowd video datasets are used to design accurate algorithms or training models for crowded scene understanding. Our overall approach is composed of two components: a procedural simulation framework for generating crowd movements and behaviors, and a procedural rendering framework to generate different videos or images. Each video or image is 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-generated behaviors with real-world background videos. We demonstrate the benefits of LCrowdV over prior lableled crowd datasets by improving the accuracy of pedestrian detection and crowd behavior classification algorithms. LCrowdV would be released on the WWW.