通过骨骼姿态分析行人手势，提升自动驾驶识别能力
Gesture Matters: Pedestrian Gesture Recognition for AVs Through Skeleton Pose Evaluation
- 基于2D姿态估计提取76个静态动态特征进行手势分类
- 手部位置与运动速度是区分手势的关键，准确率达87%
- 适合关注自动驾驶交互与行人行为理解的研究者
手势是交通中非语言交流的重要组成部分，常在正式交通规则不足时辅助行人与驾驶员沟通。当自动驾驶车辆（AVs）难以理解此类手势时，问题尤为突出。本研究提出一种基于2D姿态估计的手势分类框架，应用于WIVW数据集的真实视频序列。我们将手势分为四类：停止（Stop）、通行（Go）、致谢与问候（Thank & Greet）、无手势（No Gesture），从归一化关键点中提取76个静态与动态特征。分析表明，手部位置和运动速度在区分不同手势类别中尤为有效，分类准确率达到87%。该成果不仅增强了自动驾驶系统的感知能力，也深化了对交通场景下行人行为的理解。
原文摘要 · Abstract (English)
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.
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