RRV: A Spatiotemporal Descriptor for Rigid Body Motion Recognition
This work addresses motion recognition problems in fields like robotics or human-computer interaction, but it is incremental as it builds on existing descriptor methods with specific improvements.
The paper tackles rigid body motion recognition by introducing the RRV descriptor, which uses local translational and rotational invariants to characterize motion trajectories, and shows it outperforms previous methods in accuracy on benchmark datasets without higher computational cost.
Motion behaviors of a rigid body can be characterized by a 6-dimensional motion trajectory, which contains position vectors of a reference point on the rigid body and rotations of this rigid body over time. This paper devises a Rotation and Relative Velocity (RRV) descriptor by exploring the local translational and rotational invariants of motion trajectories of rigid bodies, which is insensitive to noise, invariant to rigid transformation and scaling. A flexible metric is also introduced to measure the distance between two RRV descriptors. The RRV descriptor is then applied to characterize motions of a human body skeleton modeled as articulated interconnections of multiple rigid bodies. To illustrate the descriptive ability of the RRV descriptor, we explore it for different rigid body motion recognition tasks. The experimental results on benchmark datasets demonstrate that this simple RRV descriptor outperforms the previous ones regarding recognition accuracy without increasing computational cost.