Work

Pedestrian trajectory prediction with convolutional neural networks

Year: 2021

Type: article

Abstract: Predicting the future trajectories of pedestrians is a challenging problem that has a range of application, from crowd surveillance to autonomous driving. In literature, methods to approach pedestrian trajectory prediction have evolved, transitioning from physics-based models to data-driven models based on recurrent neural networks. In this work, we propose a new approach to pedestrian trajectory prediction, with the introduction of a novel 2D convolutional model. This new model outperforms recurrent models, and it achieves state-of-the-art results on the ETH and TrajNet datasets. We also present an effective system to represent pedestrian positions and powerful data augmentation techniques, such as the addition of Gaussian noise and the use of random rotations, which can be applied to any model. As an additional exploratory analysis, we present experimental results on the inclusion of occupancy methods to model social information, which empirically show that these methods are ineffective in capturing social interaction.

Source: Pattern Recognition

DOI: 10.1016/j.patcog.2021.108252

Authors: Simone Zamboni, Zekarias Tilahun Kefato, Sarunas Girdzijauskas, Christoffer Norén, Laura Dal Col

Institutions: KTH Royal Institute of Technology, Scania (Sweden)

Language: English

Topic: Evacuation and Crowd Dynamics

Subfield: Safety Research

Field: Social Sciences

Domain: Social Sciences

SDG: Sustainable cities and communities

Open Access status: hybrid

Estimated APC paid: $3,100