Down-Sampling coupled to Elastic Kernel Machines for Efficient Recognition of Isolated Gestures
This work addresses the computational bottleneck in real-time gesture recognition for applications like human-computer interaction, though it is incremental as it builds on existing elastic distance methods.
The paper tackles the problem of high computational complexity in gesture recognition by introducing temporal down-sampling coupled with elastic kernel machines, showing that it significantly reduces the number of skeleton frames while maintaining good recognition rates comparable to state-of-the-art methods on two datasets.
In the field of gestural action recognition, many studies have focused on dimensionality reduction along the spatial axis, to reduce both the variability of gestural sequences expressed in the reduced space, and the computational complexity of their processing. It is noticeable that very few of these methods have explicitly addressed the dimensionality reduction along the time axis. This is however a major issue with regard to the use of elastic distances characterized by a quadratic complexity. To partially fill this apparent gap, we present in this paper an approach based on temporal down-sampling associated to elastic kernel machine learning. We experimentally show, on two data sets that are widely referenced in the domain of human gesture recognition, and very different in terms of quality of motion capture, that it is possible to significantly reduce the number of skeleton frames while maintaining a good recognition rate. The method proves to give satisfactory results at a level currently reached by state-of-the-art methods on these data sets. The computational complexity reduction makes this approach eligible for real-time applications.