# Efficient Dense Crowd Trajectory Prediction Via Dynamic Clustering

arXiv: https://arxiv.org/abs/2603.18166 · HTML: https://mlsanity.com/paper/2603.18166/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 31 references that are in the map.
- First posted on arXiv 18 Mar 2026.

## Builds on (most cited first, 20 of 20)

- [Simple Online and Realtime Tracking with a Deep Association Metric](https://mlsanity.com/paper/1703.07402/) (2017)
- [Simple Online and Realtime Tracking](https://mlsanity.com/paper/1602.00763/) (2016)
- [ByteTrack: Multi-Object Tracking by Associating Every Detection Box](https://mlsanity.com/paper/2110.06864/) (2021)
- [Observation-Centric SORT: Rethinking SORT for Robust Multi-Object Tracking](https://mlsanity.com/paper/2203.14360/) (2022)
- [BoT-SORT: Robust Associations Multi-Pedestrian Tracking](https://mlsanity.com/paper/2206.14651/) (2022)
- [CrowdHuman: A Benchmark for Detecting Human in a Crowd](https://mlsanity.com/paper/1805.00123/) (2018)
- [Human Motion Trajectory Prediction: A Survey](https://mlsanity.com/paper/1905.06113/) (2019)
- [SocialVAE: Human Trajectory Prediction using Timewise Latents](https://mlsanity.com/paper/2203.08207/) (2022)
- [Uncovering the Missing Pattern: Unified Framework Towards Trajectory Imputation and Prediction](https://mlsanity.com/paper/2303.16005/) (2023)
- [Tracking Pedestrian Heads in Dense Crowd](https://mlsanity.com/paper/2103.13516/) (2021)
- [Optical Flow Distillation: Towards Efficient and Stable Video Style Transfer](https://mlsanity.com/paper/2007.05146/) (2020)
- [MART: MultiscAle Relational Transformer Networks for Multi-agent Trajectory Prediction](https://mlsanity.com/paper/2407.21635/) (2024)
- [Grouptron: Dynamic Multi-Scale Graph Convolutional Networks for Group-Aware Dense Crowd Trajectory Forecasting](https://mlsanity.com/paper/2109.14128/) (2021)
- [Detecting Heads using Feature Refine Net and Cascaded Multi-Scale Architecture](https://mlsanity.com/paper/1803.09256/) (2018)
- [Multiple Object Forecasting: Predicting Future Object Locations in Diverse Environments](https://mlsanity.com/paper/1909.11944/) (2019)
- [Dense Point Prediction: A Simple Baseline for Crowd Counting and Localization](https://mlsanity.com/paper/2104.12505/) (2021)
- [Pedestrian Trajectory Prediction with Missing Data: Datasets, Imputation, and Benchmarking](https://mlsanity.com/paper/2411.00174/) (2024)
- [Multi-Camera Trajectory Forecasting with Trajectory Tensors](https://mlsanity.com/paper/2108.04694/) (2021)
- [Binary Quadratic Programing for Online Tracking of Hundreds of People in Extremely Crowded Scenes](https://mlsanity.com/paper/1603.09240/) (2016)
- [Efficient Dynamic Clustering: Capturing Patterns from Historical Cluster Evolution](https://mlsanity.com/paper/2203.00812/) (2022)

## Abstract (arXiv)

Crowd trajectory prediction plays a crucial role in public safety and management, where it can help prevent disasters such as stampedes. Recent works address the problem by predicting individual trajectories and considering surrounding objects based on manually annotated data. However, these approaches tend to overlook dense crowd scenarios, where the challenges of automation become more pronounced due to the massiveness, noisiness, and inaccuracy of the tracking outputs, resulting in high computational costs. To address these challenges, we propose and extensively evaluate a novel cluster-based approach that groups individuals based on similar attributes over time, enabling faster execution through accurate group summarisation. Our plug-and-play method can be combined with existing trajectory predictors by using our output centroid in place of their pedestrian input. We evaluate our proposed method on several challenging dense crowd scenes. We demonstrated that our approach leads to faster processing and lower memory usage when compared with state-of-the-art methods, while maintaining the accuracy