一种基于层次姿态的复杂动作理解方法
A Hierarchical Pose-Based Approach to Complex Action Understanding Using Dictionaries of Actionlets and Motion Poselets
摘要：
在本文中，我们介绍了一个新的层次模型，人体动作识别使用身体关节位置。我们的模型可以对视频中的复杂动作进行分类，并对组成正在执行的复杂动作的原子动作执行时空注释。也就是说，对于每个原子动作，模型通过估计其开始和结束时间来生成时间动作注释，以及通过推断执行动作所涉及的人体部位来生成空间注释。我们的模型包括三个关键的新特性：（i）它可以在没有空间监督的情况下进行训练，因为它可以仅从时间动作注释中自动发现活动的身体部位;（ii）它联合学习对身体部位和原子动作的视觉可变性进行编码的运动possible和actionlet的灵活表示;（iii）丢弃空闲或无信息的身体部位的机制，这增加了其对常见姿态估计误差的鲁棒性。我们使用多个动作识别基准评估我们的方法的性能。我们的模型始终优于基线和最先进的动作识别方法。
英文摘要：
In this paper, we introduce a new hierarchical model for human action recognition using body joint locations. Our model can categorize complex actions in videos, and perform spatio-temporal annotations of the atomic actions that compose the complex action being performed.That is, for each atomic action, the model generates temporal action annotations by estimating its starting and ending times, as well as, spatial annotations by inferring the human body parts that are involved in executing the action. our model includes three key novel properties: (i) it can be trained with no spatial supervision, as it can automatically discover active body parts from temporal action annotations only; (ii) it jointly learns flexible representations for motion poselets and actionlets that encode the visual variability of body parts and atomic actions; (iii) a mechanism to discard idle or non-informative body parts which increases its robustness to common pose estimation errors. We evaluate the performance of our method using multiple action recognition benchmarks. Our model consistently outperforms baselines and state-of-the-art action recognition methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
