Abstract
Gestures are a key component of non-verbal communication in traffic, often helping pedestrian-to-driver interactions when formal traffic rules may be insufficient. This problem becomes more apparent when autonomous vehicles (AVs) struggle to interpret such gestures. In this study, we present a gesture classification framework using 2D pose estimation applied to real-world video sequences from the WIVW dataset. We categorise gestures into four primary classes (Stop, Go, Thank & Greet, and No Gesture) and extract 76 static and dynamic features from normalised keypoints. Our analysis demonstrates that hand position and movement velocity are especially discriminative in distinguishing between gesture classes, achieving a classification accuracy score of 87%. These findings not only improve the perceptual capabilities of AV systems but also contribute to the broader understanding of pedestrian behaviour in traffic contexts.
Abstract (translated)
手势是非言语交通交流中的关键组成部分，常常在正式交通规则不足时帮助行人与司机之间的互动。当自动驾驶汽车（AV）难以解读这些手势时，这一问题变得更加明显。在这项研究中，我们提出了一种基于二维姿态估计的框架，用于从WIVW数据集中提取的真实世界视频序列的手势分类。我们将手势分为四大类（停止、前进、感谢与问候、无手势），并从归一化的关键点中抽取76个静态和动态特征。我们的分析表明，手的位置和移动速度在区分不同手势类别时尤其具有鉴别力，达到了87%的分类准确率。这些发现不仅提升了自动驾驶系统的感知能力，还为理解交通环境中行人的行为提供了更广泛的认识。
URL
https://arxiv.org/abs/2602.08479
