# Optical Flow Dataset and Benchmark for Visual Crowd Analysis

arXiv: https://arxiv.org/abs/1811.07170 · HTML: https://mlsanity.com/paper/1811.07170/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Nov 2018.

## Built on by (most cited first, 8 of 8)

- [Video Crowd Localization with Multi-focus Gaussian Neighborhood Attention and a Large-Scale Benchmark](https://mlsanity.com/paper/2107.08645/) (2021)
- [OmniFlow: Human Omnidirectional Optical Flow](https://mlsanity.com/paper/2104.07960/) (2021)
- [Joint CNN and Transformer Network via weakly supervised Learning for efficient crowd counting](https://mlsanity.com/paper/2203.06388/) (2022)
- [PromptMix: Text-to-image diffusion models enhance the performance of lightweight networks](https://mlsanity.com/paper/2301.12914/) (2023)
- [REGEN: Real-Time Photorealism Enhancement in Games via a Dual-Stage Generative Network Framework](https://mlsanity.com/paper/2508.17061/) (2025)
- [A Density-Guided Temporal Attention Transformer for Indiscernible Object Counting in Underwater Video](https://mlsanity.com/paper/2403.03461/) (2024)
- [HyPER-GAN: Hybrid Patch-Based Image-to-Image Translation for Real-Time Photorealism Enhancement in Game Engines](https://mlsanity.com/paper/2603.10604/) (2026)
- [Pix2Pix-Hybrid: Structure-Guided Conditional Synthesis of Hajj Crowd Images with Multi-Channel Conditioning and Weak Attribute Supervision](https://mlsanity.com/paper/2606.14297/) (2026)

## Abstract (arXiv)

The performance of optical flow algorithms greatly depends on the specifics of the content and the application for which it is used. Existing and well established optical flow datasets are limited to rather particular contents from which none is close to crowd behavior analysis; whereas such applications heavily utilize optical flow. We introduce a new optical flow dataset exploiting the possibilities of a recent video engine to generate sequences with ground-truth optical flow for large crowds in different scenarios. We break with the development of the last decade of introducing ever increasing displacements to pose new difficulties. Instead we focus on real-world surveillance scenarios where numerous small, partly independent, non rigidly moving objects observed over a long temporal range pose a challenge. By evaluating different optical flow algorithms, we find that results of established datasets can not be transferred to these new challenges. In exhaustive experiments we are able to provide new insight into optical flow for crowd analysis. Finally, the results have been validated on the real-world UCF crowd tracking benchmark while achieving competitive results compared to more sophisticated state-of-the-art crowd tracking approaches.