# Crowd Behavior Analysis: A Review where Physics meets Biology

arXiv: https://arxiv.org/abs/1511.06586 · HTML: https://mlsanity.com/paper/1511.06586/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Nov 2015.

## Built on by (most cited first, 5 of 5)

- [Visual Crowd Analysis: Open Research Problems](https://mlsanity.com/paper/2308.10677/) (2023)
- [Rethinking Trajectory Prediction via "Team Game"](https://mlsanity.com/paper/2210.08793/) (2022)
- [The Garden of Forking Paths: Observing Dynamic Parameters Distribution in Large Language Models](https://mlsanity.com/paper/2403.08739/) (2024)
- [Integrating GNN and Neural ODEs for Estimating Non-Reciprocal Two-Body Interactions in Mixed-Species Collective Motion](https://mlsanity.com/paper/2405.16503/) (2024)
- [Density-based clustering with fully-convolutional networks for crowd flow detection from drones](https://mlsanity.com/paper/2301.04937/) (2023)

## Abstract (arXiv)

Although the traits emerged in a mass gathering are often non-deliberative, the act of mass impulse may lead to irre- vocable crowd disasters. The two-fold increase of carnage in crowd since the past two decades has spurred significant advances in the field of computer vision, towards effective and proactive crowd surveillance. Computer vision stud- ies related to crowd are observed to resonate with the understanding of the emergent behavior in physics (complex systems) and biology (animal swarm). These studies, which are inspired by biology and physics, share surprisingly common insights, and interesting contradictions. However, this aspect of discussion has not been fully explored. Therefore, this survey provides the readers with a review of the state-of-the-art methods in crowd behavior analysis from the physics and biologically inspired perspectives. We provide insights and comprehensive discussions for a broader understanding of the underlying prospect of blending physics and biology studies in computer vision.