Human Behaviour Recognition Using Deep Learning

摘要

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.