From Seeing to Recognising-An Extended Self-Organizing Map for Human Postures Identification

Abstract

The letter presents a dedicated method for recognizing human postures using classification and clustering options. The ultimate goal of the research is to recognise human actions based on posture sequences. Such a task imposes expectations on the developed method. For this purpose, a Sparse Autoencoder combined with a Self-Organized Map (SOM) is proposed. SOM is equipped with an additional layer of post-labeling or clustering. This entire structure is called the extended SOM. Two task-oriented modifications are applied to improve SOM performance – a dedicated angular distance measure and a neighbourhood function for updating the SOM weights. The research contribution is the concept of extended SOM, which is trained using unlabeled data and classifies or clusters the human postures. The Sparse Autoencoder preserves the characteristics of the data while reducing its dimensionality. Better classification efficiency of the developed method is demonstrated compared to other representative methods. Ablation studies illustrate how the introduced modifications improve classification results. The developed method is characterised by good resolution in distinguishing postures. A discussion of the concept's usefulness is provided at the end of the letter.