Performance Comparison of Human Activity Recognition for Unmanned Retails

Abstract

Lately, the broad usage of technology in almost all aspects of life has led to the increase in research supporting technology advancement. One of these research topics is Human Activity Recognition (HAR) with diverse applicability which include and not limited to video surveillance, healthcare and education. In this paper, we present a study based on human activity recognition while employing the Kinect RGB and Depth sensor camera to recognize seven different human activities (7 classes). The joint angles extracted from the Kinect depth sensor each has 3 axes (X, Y, Z) for the 8 limbs employed in our experiment as the feature vectors. For the purpose of classifying the human activities, we train and test with 3 different state of the art recurrent neural network models (GRU, LSTM, Bi-LSTM). The comparison of the 3 recurrent neural network models shows that LSTM has a higher human activity classification accuracy at 96% and using the confusion matrix as the performance metric for all the models, we show classification per activity.