Human Trajectory Forecasting in Crowds: A Deep Learning Perspective

TL;DR

This paper reviews deep learning approaches for human trajectory forecasting in crowds, introduces new methods for modeling social interactions, and presents a comprehensive benchmark with novel metrics to evaluate socially acceptable predictions.

Contribution

It provides an in-depth analysis of existing methods, proposes two new knowledge-based data-driven models, and introduces TrajNet++, a large-scale benchmark with new evaluation metrics.

Findings

Our methods outperform baselines on real-world datasets.

Proposed metrics effectively evaluate social acceptability.

TrajNet++ enables comprehensive comparison of models.

Abstract

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.