Compressive Sensing of Time Series for Human Action Recognition

Abstract

Compressive Sensing (CS) is an emerging signal processing technique where a sparse signal is reconstructed from a small set of random projections. In the recent literature, CS techniques have demonstrated promising results for signal compression and reconstruction. However, their potential as dimensionality reduction techniques for time series has not been significantly explored to date. To this aim, this work investigates the suitability of compressive-sensed time series in an application of human action recognition. In the paper, results from several experiments are presented: (1) in a first set of experiments, the time series are transformed into the CS domain and fed into a hidden Markov model (HMM) for action recognition, (2) in a second set of experiments, the time series are explicitly reconstructed after CS compression and then used for recognition, (3) in the third set of experiments, the time series are compressed by a hybrid CS-Haar basis prior to input into HMM, (4) in the fourth set, the time series are reconstructed from the hybrid CS-Haar basis and used for recognition. We further compare these approaches with alternative techniques such as sub-sampling and filtering. Results from our experiments show unequivocally that the application of CS does not degrade the recognition accuracy, rather, it often increases it. This proves that CS can provide a desirable form of dimensionality reduction in pattern recognition over time series.