Lie-X: Depth Image Based Articulated Object Pose Estimation, Tracking, and Action Recognition on Lie Groups
It addresses a challenging problem in computer vision for applications like animal behavior analysis and human-computer interaction, but appears incremental as it builds on existing methods.
The paper tackles the unified problems of pose estimation, tracking, and action recognition for articulated objects from depth images by proposing a paradigm based on Lie group theory, achieving competitive results compared to state-of-the-art methods on lab animals and human hands.
Pose estimation, tracking, and action recognition of articulated objects from depth images are important and challenging problems, which are normally considered separately. In this paper, a unified paradigm based on Lie group theory is proposed, which enables us to collectively address these related problems. Our approach is also applicable to a wide range of articulated objects. Empirically it is evaluated on lab animals including mouse and fish, as well as on human hand. On these applications, it is shown to deliver competitive results compared to the state-of-the-arts, and non-trivial baselines including convolutional neural networks and regression forest methods.