Pedestrian trajectory prediction with convolutional neural networks

PATTERN RECOGNITION

卷:121

Article

Zamboni, Simone¹  Kefato, Zekarias Tilahun¹  Girdzijauskas, Sarunas¹  Noren, Christoffer²  Dal Col, Laura² 

[1] KTH Div Software & Comp Syst, Stockholm, Sweden

[2] Autonomous Transport Syst Scania CV AB, Sodertalje, Sweden

关键词: Trajectory prediction;    Pedestrian prediction;    Convolutional neural networks;   

DOI  :  10.1016/j.patcog.2021.108252

来源: Elsevier

【 摘 要 】

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. (c) 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )

【 授权许可】

Free