Scopus Indexed Paper

Paper Details


Title
ExNET: Deep Neural Network for Exercise Pose Detection
Abstract
Pose detection estimate human activity in images or video frames using computer vision technique. Pose detection has many applications, such as body to augmented reality, fitness, animation etc. ExNET represents a way to detect human pose from 2D human exercises image using Convolutional Neural Network. In recent time Deep Learning based systems are making it possible to detect human exercise poses from images. We refer to the model we have built for this task as ExNET: Deep Neural Network for Exercise Pose Detection. We have evaluated our proposed model on our own dataset that contains a total of 2000 images. And those images are distributed into 5 classes as well as images are divided into training and test dataset, and obtained improved performance. We have conducted various experiments with our model on the test dataset, and finally got the best accuracy of 82.68%.
Keywords
Human pose detection, Object detection, Deep learning, Exercise Pose Detection
Authors
Sadeka HaqueE, AKM Shahariar Azad Rabby, Monira Akter Laboni, Nafis Neehal, Syed Akhter Hossain
Phone
Journal or Conference Name
Communications in Computer and Information Science
Publish Year
2019
Indexing
Scopus