论文标题
具有行动意识形态的Videopose3D的培训方法
A Training Method For VideoPose3D With Ideology of Action Recognition
论文作者
论文摘要
视频中的行动识别和姿势估计与了解人类动作密切相关，但是更多的文献集中于如何仅从行动识别中解决姿势估计任务。这项研究显示了基于行动识别的VideoPose3D的更快，更灵活的培训方法。该模型具有与将要估计的类型相同的动作类型，并且可以单独训练不同类型的动作。有证据表明，对于常见的姿势估计任务，该模型需要相对较少的数据才能通过原始研究进行相似的结果，而对于以动作为导向的任务，它在MPJPE的速度误差上以有限的接受场大小和训练时间的范围优于原始研究。该模型可以处理面向动作的姿势估计问题。
Action recognition and pose estimation from videos are closely related to understand human motions, but more literature focuses on how to solve pose estimation tasks alone from action recognition. This research shows a faster and more flexible training method for VideoPose3D which is based on action recognition. This model is fed with the same type of action as the type that will be estimated, and different types of actions can be trained separately. Evidence has shown that, for common pose-estimation tasks, this model requires a relatively small amount of data to carry out similar results with the original research, and for action-oriented tasks, it outperforms the original research by 4.5% with a limited receptive field size and training epoch on Velocity Error of MPJPE. This model can handle both action-oriented and common pose-estimation problems.
