# Trajectory Prediction by Coupling Scene-LSTM with Human Movement LSTM

arXiv: https://arxiv.org/abs/1908.08908 · HTML: https://mlsanity.com/paper/1908.08908/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Aug 2019.

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

- [Investigating Pose Representations and Motion Contexts Modeling for 3D Motion Prediction](https://mlsanity.com/paper/2112.15012/) (2021)
- [CausalAgents: A Robustness Benchmark for Motion Forecasting using Causal Relationships](https://mlsanity.com/paper/2207.03586/) (2022)
- [Unlimited Neighborhood Interaction for Heterogeneous Trajectory Prediction](https://mlsanity.com/paper/2108.00238/) (2021)
- [Trajectory Prediction for Autonomous Driving: Progress, Limitations, and Future Directions](https://mlsanity.com/paper/2503.03262/) (2025)
- [Vision-based Multi-future Trajectory Prediction: A Survey](https://mlsanity.com/paper/2302.10463/) (2023)

## Abstract (arXiv)

We develop a novel human trajectory prediction system that incorporates the scene information (Scene-LSTM) as well as individual pedestrian movement (Pedestrian-LSTM) trained simultaneously within static crowded scenes. We superimpose a two-level grid structure (grid cells and subgrids) on the scene to encode spatial granularity plus common human movements. The Scene-LSTM captures the commonly traveled paths that can be used to significantly influence the accuracy of human trajectory prediction in local areas (i.e. grid cells). We further design scene data filters, consisting of a hard filter and a soft filter, to select the relevant scene information in a local region when necessary and combine it with Pedestrian-LSTM for forecasting a pedestrian's future locations. The experimental results on several publicly available datasets demonstrate that our method outperforms related works and can produce more accurate predicted trajectories in different scene contexts.