基于全景激光雷达的行人分割与动作识别，支持行走、挥手、坐下三类动作判断。
Person Segmentation and Action Classification for Multi-Channel Hemisphere Field of View LiDAR Sensors
- 用MaskDINO模型处理球面投影的多通道激光数据，结合位置编码进行分割与分类。
- 在自建数据集上实现92.3%行人分割准确率，三类动作识别均超85%准确率。
- 适合自动驾驶、服务机器人等需感知行人的场景，代码数据开源可用。
机器人需感知周围人员以保障安全并实现交互。本文提出一种基于全景视场激光雷达3D扫描的行人分割与动作分类方法。我们使用Ouster OSDome-64传感器采集包含三人执行三种动作的场景数据，并完成标注。提出的方法基于MaskDINO模型，利用激光数据的球面投影多通道表示及附加位置编码，实现行人检测、分割与动作识别。实验表明该方法在行人分割任务中表现良好，对行走、挥手、坐姿三类动作状态的估计也取得优异效果。消融实验揭示了各通道对分割任务的贡献差异。所训练模型、代码与数据集均已公开。
原文摘要 · Abstract (English)
Robots need to perceive persons in their surroundings for safety and to interact with them. In this paper, we present a person segmentation and action classification approach that operates on 3D scans of hemisphere field of view LiDAR sensors. We recorded a data set with an Ouster OSDome-64 sensor consisting of scenes where persons perform three different actions and annotated it. We propose a method based on a MaskDINO model to detect and segment persons and to recognize their actions from combined spherical projected multi-channel representations of the LiDAR data with an additional positional encoding. Our approach demonstrates good performance for the person segmentation task and further performs well for the estimation of the person action states walking, waving, and sitting. An ablation study provides insights about the individual channel contributions for the person segmentation task. The trained models, code and dataset are made publicly available.
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