Purpose

This paper proposes a novel method for human hands tracking using data from an event camera. The event camera detects changes in brightness, measuring motion, with low latency, no motion blur, low power consumption and high dynamic range. Captured frames are analysed using lightweight algorithms reporting three-dimensional (3D) hand position data. The chosen pick-and-place scenario serves as an example input for collaborative human–robot interactions and in obstacle avoidance for human–robot safety applications.

Design/methodology/approach

Events data are pre-processed into intensity frames. The regions of interest (ROI) are defined through object edge event activity, reducing noise. ROI features are extracted for use in-depth perception.

Findings

Event-based tracking of human hand demonstrated feasible, in real time and at a low computational cost. The proposed ROI-finding method reduces noise from intensity images, achieving up to 89% of data reduction in relation to the original, while preserving the features. The depth estimation error in relation to ground truth (measured with wearables), measured using dynamic time warping and using a single event camera, is from 15 to 30 millimetres, depending on the plane it is measured.

Originality/value

Tracking of human hands in 3 D space using a single event camera data and lightweight algorithms to define ROI features (hands tracking in space).

中文翻译：

---

基于事件的人手跟踪

目的

本文提出了一种使用来自事件相机的数据进行人手跟踪的新方法。事件相机检测亮度变化，测量运动，具有低延迟、无运动模糊、低功耗和高动态范围。使用轻量级算法分析捕获的帧，报告三维 (3D) 手部位置数据。所选的拾放场景可作为人机协作交互和人机安全应用避障的示例输入。

设计/方法/方法

事件数据被预处理成强度帧。感兴趣区域 (ROI) 是通过对象边缘事件活动来定义的，从而减少噪声。提取 ROI 特征以用于深度感知。

发现

基于事件的人手跟踪被证明是可行的，实时且计算成本低。所提出的 ROI 查找方法减少了强度图像中的噪声，与原始图像相比，实现了高达 89% 的数据减少，同时保留了特征。使用动态时间扭曲和使用单个事件相机测量的与地面实况（使用可穿戴设备测量）相关的深度估计误差为 15 到 30 毫米，具体取决于测量的平面。

原创性/价值

使用单事件相机数据和轻量级算法在 3D 空间中跟踪人手，以定义 ROI 特征（空间中的手跟踪）。