Human Behaviour Recognition Using Deep Learning
Lu Jia, Wei Qi Yan, Minh Nguyen · 2018
Traditional human behaviour recognition is mostly based on global features of digital images. Nowadays, with the increase of computing power and processing capacity, deep neural networks (DNNs) acquire a high possibility to detect any objects, which have effectively led to a new era of machine learning. In this paper, we investigated a human behaviour recognition using deep learning based on YOLOv3 model. After a number of experiments conducted, our YOLOv3 model had shown to achieve 80.20% of accuracy in human behaviour recognition with the speed of approximate 15 fps using GPU acceleration. Our direct contributions are: (1) data augment and collection, (2) adjusting deep neural network structures, and (3) superior performance in evaluations for our proposed deep learning model.