Exnet

dc.contributor.authorHaque, Sadeka
dc.contributor.authorRabby, AKM Shahariar Azad
dc.contributor.authorLaboni, Monira Akter
dc.contributor.authorNeehal, Nafis
dc.contributor.authorHossain, Syed Akhter
dc.date.accessioned2022-02-19T11:58:06Z
dc.date.available2022-02-19T11:58:06Z
dc.date.issued2019-07-20
dc.description.abstractPose 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%.
dc.identifier.otherhttp://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/7207
dc.identifier.urihttp://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/7207
dc.language.isoen_US
dc.publisherCommunications in Computer and Information Science, Springer
dc.sourceDIU Institutional Repository
dc.subjectHuman pose detection
dc.subjectObject detection
dc.subjectDeep learning
dc.subjectExercise pose detection
dc.titleExnet
dc.title.alternativeDeep Neural Network for Exercise Pose Detection
dc.typeArticle

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