Trajectory Prediction by Coupling Scene-LSTM with Human Movement LSTM
This addresses trajectory prediction in crowded scenes for applications like autonomous driving or surveillance, but it is incremental as it builds on existing LSTM-based methods with scene integration.
The paper tackles human trajectory prediction by integrating scene information (Scene-LSTM) with individual pedestrian movement (Pedestrian-LSTM) using a two-level grid structure and scene data filters, resulting in outperforming related works and producing more accurate predicted trajectories across datasets.
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.