Trajectory Prediction by Coupling Scene-LSTM with Human Movement LSTM

TL;DR

This paper introduces a novel trajectory prediction system combining scene context and individual movement data using Scene-LSTM and Pedestrian-LSTM, significantly improving accuracy in crowded scenes.

Contribution

It proposes a new integrated LSTM-based framework with scene encoding and data filtering to enhance trajectory prediction accuracy.

Findings

Outperforms existing methods on public datasets

Effectively models scene influence on pedestrian movement

Improves prediction accuracy in complex scenes

Abstract

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.