Papers › Human Trajectory Forecasting in Crowds: A Deep Learning Perspective
Human Trajectory Forecasting in Crowds: A Deep Learning Perspective
Parth Kothari, Sven Kreiss, Alexandre Alahi
Since the past few decades, human trajectory forecasting has been a field of active research owing to its numerous real-world applications: evacuation situation analysis, deployment of intelligent transport systems, traffic operations, to name a few. Early works handcrafted this representation based on domain knowledge. However, social interactions in crowded environments are not only diverse but often subtle. Recently, deep learning methods have outperformed their handcrafted counterparts, as they learned about human-human interactions in a more generic data-driven fashion. In this work, we present an in-depth analysis of existing deep learning-based methods for modelling social interactions. We propose two knowledge-based data-driven methods to effectively capture these social interactions. To objectively compare the performance of these interaction-based forecasting models, we develop a large scale interaction-centric benchmark TrajNet++, a significant yet missing component in the field of human trajectory forecasting. We propose novel performance metrics that evaluate the ability of a model to output socially acceptable trajectories. Experiments on TrajNet++ validate the need for our proposed metrics, and our method outperforms competitive baselines on both real-world and synthetic datasets.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Trajectory Forecasting | TrajNet++ | Social LSTM | COL | 7.59 | #3 of 3 | Archive leaderboard | report |
| Trajectory Forecasting | TrajNet++ | Social LSTM | FDE | 1.17 | #3 of 3 | Archive leaderboard | report |
| Trajectory Prediction | TrajNet++ | Social LSTM | COL | 7.59 | #3 of 3 | Archive leaderboard | report |
| Trajectory Prediction | TrajNet++ | Social LSTM | FDE | 1.17 | #3 of 3 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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