SITUATE: Indoor Human Trajectory Prediction through Geometric Features and Self-Supervised Vision Representation

TL;DR

SITUATE introduces a novel indoor human trajectory prediction method that combines geometric features and self-supervised vision representations, achieving state-of-the-art results on indoor datasets and generalizing well outdoors.

Contribution

The paper presents a new approach leveraging geometric features and self-supervised vision to improve indoor trajectory prediction, addressing the unique challenges of indoor environments.

Findings

Achieves state-of-the-art performance on THOR and Supermarket datasets.

Demonstrates better generalization to outdoor scenarios compared to outdoor-oriented models.

Effectively models intrinsic symmetries and human movements in indoor spaces.

Abstract

Patterns of human motion in outdoor and indoor environments are substantially different due to the scope of the environment and the typical intentions of people therein. While outdoor trajectory forecasting has received significant attention, indoor forecasting is still an underexplored research area. This paper proposes SITUATE, a novel approach to cope with indoor human trajectory prediction by leveraging equivariant and invariant geometric features and a self-supervised vision representation. The geometric learning modules model the intrinsic symmetries and human movements inherent in indoor spaces. This concept becomes particularly important because self-loops at various scales and rapid direction changes often characterize indoor trajectories. On the other hand, the vision representation module is used to acquire spatial-semantic information about the environment to predict users' future locations more accurately. We evaluate our method through comprehensive experiments on the two most famous indoor trajectory forecasting datasets, i.e., TH\"OR and Supermarket, obtaining state-of-the-art performance. Furthermore, we also achieve competitive results in outdoor scenarios, showing that indoor-oriented forecasting models generalize better than outdoor-oriented ones. The source code is available at https://github.com/intelligolabs/SITUATE.