# Crowd Density Forecasting by Modeling Patch-based Dynamics

arXiv: https://arxiv.org/abs/1911.09814 · HTML: https://mlsanity.com/paper/1911.09814/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Nov 2019.

## Built on by (most cited first, 3 of 3)

- [End-to-End Trajectory Distribution Prediction Based on Occupancy Grid Maps](https://mlsanity.com/paper/2203.16910/) (2022)
- [Multi-Camera Trajectory Forecasting with Trajectory Tensors](https://mlsanity.com/paper/2108.04694/) (2021)
- [CrowdMAC: Masked Crowd Density Completion for Robust Crowd Density Forecasting](https://mlsanity.com/paper/2407.14725/) (2024)

## Abstract (arXiv)

Forecasting human activities observed in videos is a long-standing challenge in computer vision, which leads to various real-world applications such as mobile robots, autonomous driving, and assistive systems. In this work, we present a new visual forecasting task called crowd density forecasting. Given a video of a crowd captured by a surveillance camera, our goal is to predict how that crowd will move in future frames. To address this task, we have developed the patch-based density forecasting network (PDFN), which enables forecasting over a sequence of crowd density maps describing how crowded each location is in each video frame. PDFN represents a crowd density map based on spatially overlapping patches and learns density dynamics patch-wise in a compact latent space. This enables us to model diverse and complex crowd density dynamics efficiently, even when the input video involves a variable number of crowds that each move independently. Experimental results with several public datasets demonstrate the effectiveness of our approach compared with state-of-the-art forecasting methods.