Linear-projection-based classification of human postures in time-of-flight data

Abstract

This paper presents a simple yet effective approach for classification of human postures by using a time-of-flight camera. We investigate and adopt linear projection techniques such as Locality Preserving Projections (LPP), Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA), which are more widespread in face recognition and other pattern recognition tasks. We analyze the relations between LPP and LDA and show experimentally that using LPP in a supervised manner effectively yields very similar results as LDA, implying that LPP may be regarded as a generalization of LDA. Features for offline training and online classification are created by adopting common image processing techniques such as background-subtraction and blob detection to the time-of-flight data.