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

翻译：本文提出了一种利用事件相机数据进行人手跟踪的新方法。事件相机通过检测亮度变化来测量运动，具有低延迟、无运动模糊、低功耗和高动态范围等优点。所捕获的帧通过轻量级算法进行分析，输出三维手部位置数据。所选的拾取与放置场景作为协作型人机交互及人机安全应用中避障的示例输入。事件数据被预处理为强度帧，通过目标边缘事件活动定义感兴趣区域以降低噪声，并提取感兴趣区域特征用于深度感知。基于事件的人手跟踪被证明具有可行性，可实现实时运行且计算成本低。所提出的感兴趣区域检测方法可减少强度图像的噪声，相较于原始数据实现高达89%的数据压缩，同时保留关键特征。使用单台事件相机，通过动态时间规整测量，深度估计误差（与穿戴设备测得的地面真值比较）范围为15至30毫米，具体取决于测量平面。通过结合单台事件相机数据与轻量级算法定义感兴趣区域特征，实现了三维空间中的人手跟踪。