面向航天测发任务的动作识别与追踪研究

航天测发任务精度要求高、任务周期长，且长期暴露在阳光直射下。为提高测发任务的成功率，在虚拟工作环境中进行无接触式动作标定与矫正是一种高效的方式。针对航天工作人员动作的实时动作追踪问题，提出了一种关键帧优化的动作识别算法。依据深度图像中的骨骼数据，提取骨骼特征，使用特征阈值提取关键帧。将关键帧的特征数据输入双向长短期记忆网络，优化整体动作识别的精确度。数据驱动的骨骼识别与动作追踪，能有效识别航天工作人员的动作，辅助其更高效、安全地完成测发任务。

Research on Motion Recognition and Tracking for Space Survey and Launch Tasks

Space survey and launch task has high precision and long cycle, and needs to be exposed to direct sunlight for a long time, so that the non-contact action calibration and comparison in a virtual working environment is an efficient way to improve the mission completion success rate. Aiming at the real-time motion tracking of aerospace personnel, a keyframe optimization algorithm for the action recognition is proposed. According to the bone data in the depth image, the bone features are extracted, and the keyframes are extracted by the feature threshold. The characteristic data of the keyframe is input into bi-directional long short-term memory to optimize the accuracy of the overall action recognition. Data-driven bone recognition and motion tracking can effectively identify the movements of the aerospace personnel to complete the related tasks more efficiently and safely.