Signal Alignment for Humanoid Skeletons via the Globally Optimal Reparameterization Algorithm

by   Thomas W. Mitchel, et al.

The general ability to analyze and classify the 3D kinematics of the human form is an essential step in the development of socially adept humanoid robots. A variety of different types of signals can be used by machines to represent and characterize actions such as RGB videos, infrared maps, and optical flow. In particular, skeleton sequences provide a natural 3D kinematic description of human motions and can be acquired in real time using RGB+D cameras. Moreover, skeleton sequences are generalizable to characterize the motions of both humans and humanoid robots. The Globally Optimal Reparameterization Algorithm (GORA) is a novel, recently proposed algorithm for signal alignment in which signals are reparameterized to a globally optimal universal standard timescale (UST). Here, we introduce a variant of GORA for humanoid action recognition with skeleton sequences, which we call GORA-S. We briefly review the algorithm's mathematical foundations and contextualize them in the problem of action recognition with skeleton sequences. Subsequently, we introduce GORA-S and discuss parameters and numerical techniques for its effective implementation. We then compare its performance with that of the DTW and FastDTW algorithms, in terms of computational efficiency and accuracy in matching skeletons. Our results show that GORA-S attains a complexity that is significantly less than that of any tested DTW method. In addition, it displays a favorable balance between speed and accuracy that remains invariant under changes in skeleton sampling frequency, lending it a degree of versatility that could make it well-suited for a variety of action recognition tasks.


The Globally Optimal Reparameterization Algorithm: an Alternative to Fast Dynamic Time Warping for Action Recognition in Video Sequences

Signal alignment has become a popular problem in robotics due in part to...

Skeletal Movement to Color Map: A Novel Representation for 3D Action Recognition with Inception Residual Networks

We propose a novel skeleton-based representation for 3D action recogniti...

Infrared and 3D skeleton feature fusion for RGB-D action recognition

A challenge of skeleton-based action recognition is the difficulty to cl...

Human activity recognition from skeleton poses

Human Action Recognition is an important task of Human Robot Interaction...

Learning discriminative trajectorylet detector sets for accurate skeleton-based action recognition

The introduction of low-cost RGB-D sensors has promoted the research in ...

Gimme Signals: Discriminative signal encoding for multimodal activity recognition

We present a simple, yet effective and flexible method for action recogn...

Real-time Human Action Recognition Using Locally Aggregated Kinematic-Guided Skeletonlet and Supervised Hashing-by-Analysis Model

3D action recognition is referred to as the classification of action seq...

Please sign up or login with your details

Forgot password? Click here to reset