Skeleton-based Recognition of Pedestrian Crossing Intention using Attention Graph Neural Networks

Abstract

Besides the ability to automatically detect and localize on the road, self-driving cars need to observe and understand pedestrian attention to ensure safe operations. In this study, a compact skeleton-based method to predict pedestrian crossing intention is presented. The skeleton data is first extracted using a state-of-the-art pose estimation method. Then, the proposed approach combines graph neural networks, self-attention mechanisms, and temporal convolutions to create distinctive representations of pedestrian moving skeleton sequences. The crossing intention of people is classified based on the extracted features. The experiments demonstrate competitive results with previous methods on the public JAAD dataset.