Deep learning-enabled video-based motion tracking for human-robot interaction in assembly tasks

基于深度学习的基于视频的运动跟踪，用于装配任务中的人机交互

• 批准号: 2446548
• 金额: --
• 依托单位: Queen's University Belfast
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2446548
• 关键词: Deep; learning; enabled; video; based

Manufacturing environments of the future will involve humans interacting with robots at close distance. Optimisation of these interactions will require continuous recordings of human movements, to feed into the control algorithms governing the robot movements. Although commercial motion tracking systems are available, these cannot practically be applied in most manufacturing environments, since these are costly and require sensors to be attached to the body at precisely specified positions. Modern computational tools for extracting patterns from images may provide a viable alternative. In recent years, machine learning has been used to extract body parts and body postures from images of human or animal bodies. When applied to videos - sequences of images - this method potentially affords extracting 3D body movements without having to attach sensors to the body. This, however, is not yet optimized to afford the aforementioned applications and the current PhD project will address several outstanding challenges in this field. It will for instance focus on improving training data based on video recordings synchronised with state-of-the-art full body motion tracking and appropriating the algorithms to account for motion patterns across frames.

未来的制造环境将涉及人类与机器人近距离互动。这些交互的优化将需要连续记录人类运动，以输入控制机器人运动的控制算法。虽然商业运动跟踪系统是可用的，但是这些实际上不能应用于大多数制造环境中，因为这些是昂贵的并且需要传感器在精确指定的位置处附接到身体。从图像中提取图案的现代计算工具可能提供可行的替代方案。近年来，机器学习已被用于从人体或动物体的图像中提取身体部位和身体姿势。当应用于视频（图像序列）时，这种方法可能提供提取3D身体运动，而无需将传感器连接到身体上。然而，这还没有优化，以提供上述应用程序和目前的博士项目将解决在这一领域的几个突出的挑战。例如，它将专注于改进基于与最先进的全身运动跟踪同步的视频记录的训练数据，并将算法用于跨帧的运动模式。