Encoding human actions with a frequency domain approach

In this work, we propose a Frequency-based Action Descriptor (FADE) to represent human actions. In robotics, with the development of Programming by Demonstration (PbD) methods, representing and recognizing large sets of actions has become crucial to build autonomous systems that learn from humans. The FADE descriptor leverages Fast Fourier Transform (FFT) for action representation and is combined with the Manhattan distance for measuring similarities between actions. It is characterized by a low time and space complexity and is particularly suitable for classification of human actions. For clustering problems, we propose a modified version of FADE, called Uncompressed-FADE (U-FADE), which performs well in combination with Spectral Clustering algorithms at the price of a reduced compression. We compare FADE with action descriptors based on Singular Value Decomposition (SVD) and Hidden Markov Models (HMM) on the entire HDM05 motion capture database. Despite the high dimensionality of the problem, we obtained on the entire database a promising recognition rate of 78% combining FADE with a simple 1-NN classification algorithm. Furthermore, we achieved a rate of 98% on a small action set and 88% on a medium action set.

Authors

• Dharmil Shah
• Pietro Falco
• Matteo Saveriano
• Dongheui Lee

Keywords

• Databases
• Hidden Markov models
• Frequency-domain analysis
• Scalability
• Complexity theory
• Service robots
• Human Activities
• Frequency Domain
• Fourier Transform
• Time And Space
• Fast Fourier Transform
• Hidden Markov Model
• Time Complexity
• Singular Value Decomposition
• Motion Capture
• Space Complexity
• Manhattan Distance
• Action Representation
• Action Classes
• Entire Database
• Spectral Clustering
• Spectral Clustering Algorithm
• Support Vector Machine
• Entire Dataset
• Column Vector
• Small Datasets
• Unsupervised Learning
• Human Motion
• Unsupervised Methods
• N Log N
• Joint Angles
• Dynamic Time Warping
• Extreme Learning Machine
• Human Activity Recognition
• Number Of Joints