Empirical Evaluation on Deep Learning of Depth Feature for Human Activity Recognition

Abstract

In the field of computer vision, there are two emerging approaches that have drawn much attention, and they have recently become popular way to solve various kinds of recognition problem. The first approach is unsupervised feature learning based on deep learning technique, and second approach is to conduct recognition using depth information thank to recent progress in depth sensor. At this point, it seems reasonable that one is curious about effectiveness of deep learning from raw depth data. However, a few researches have attempted to learn depth features with a deep network, and the validity has not been well studied in terms of quantitative analysis. To this end, we learned depth features for human activity recognition using existing deep learning algorithm and evaluated effectiveness of the learned depth feature on activity recognition. Furthermore, we provide analysis in detail and valuable discussion with additional experiments.