Human activity recognition based on independent depth silhouette components and optical flow features

Abstract

In this work, we propose a novel method that utilises video-based depth and optical flow information of human body movement for human activity recognition. The recognition method utilises independent components (ICs) of depth silhouettes and optical flow-based motion features from a series of depth images and discrete hidden Markov models (HMMs) for recognition. The IC features are extracted from a collection of depth silhouettes containing various human activities, over which linear discriminant analysis (LDA) is performed for better classification. Furthermore, to improve the recognition performance, optical flow-based motion features extracted from the consecutive depth silhouettes are utilised in an augmented form. In addition, discrete HMMs are employed to recognise and model the time-sequential features of human activities. Our results show that the depth silhouette feature-based approach provides better human activity recognition than most generally used binary silhouette-based approaches, and that the augmented depth IC and optical flow features provide additional improvements.

Keywords:

• human activity recognition
• depth information
• PCA
• ICA
• LDA
• optical flows
• HMM

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) (No. 2011-0001031).