Scene-LSTM: A Model for Human Trajectory Prediction

TL;DR

This paper introduces Scene-LSTM, a novel human trajectory prediction model that integrates scene context and human movement patterns using coupled LSTMs and a grid-based scene representation, significantly improving prediction accuracy.

Contribution

The paper presents a new scene-aware LSTM framework with a scene data filter and grid structure, enhancing trajectory prediction over existing methods.

Findings

Reduces location displacement errors compared to related methods

Achieves about 80% error reduction over social interaction models

Effective in crowded scenes from UCY and ETH datasets

Abstract

We develop a human movement trajectory prediction system that incorporates the scene information (Scene-LSTM) as well as human movement trajectories (Pedestrian movement LSTM) in the prediction process within static crowded scenes. We superimpose a two-level grid structure (scene is divided into grid cells each modeled by a scene-LSTM, which are further divided into smaller sub-grids for finer spatial granularity) and explore common human trajectories occurring in the grid cell (e.g., making a right or left turn onto sidewalks coming out of an alley; or standing still at bus/train stops). Two coupled LSTM networks, Pedestrian movement LSTMs (one per target) and the corresponding Scene-LSTMs (one per grid-cell) are trained simultaneously to predict the next movements. We show that such common path information greatly influences prediction of future movement. We further design a scene data filter that holds important non-linear movement information. The scene data filter allows us to select the relevant parts of the information from the grid cell's memory relative to a target's state. We evaluate and compare two versions of our method with the Linear and several existing LSTM-based methods on five crowded video sequences from the UCY [1] and ETH [2] datasets. The results show that our method reduces the location displacement errors compared to related methods and specifically about 80% reduction compared to social interaction methods.