Human Activity Recognition Using Combinatorial Deep Belief Networks

Abstract

Human activity recognition is a topic undergoing a great amount of research. The main reason for that is the number of practical applications that are developed using activity recognition as the base. This paper proposes an approach to human activity recognition using a combination of deep belief networks. One network is used to obtain features from motion and to do this we propose a modified Weber descriptor. Another network is used to obtain features from images and to do this we propose the modification of the standard local binary patterns descriptor to obtain a concatenated histogram of lower dimensions. This helps to encode spatial and temporal information of various actions happening in a frame. This further helps to overcome the dimensionality problem that occurs with LBP. The features extracted are then passed onto a CNN that classifies the activity. Few standard activities are considered such as walking, sprinting, hugging etc. Results showed that the proposed algorithm gave a high level of accuracy for classification.