Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162670
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dc.contributor.authorYang, Dorwin Junwenen_US
dc.date.accessioned2022-11-03T01:39:47Z-
dc.date.available2022-11-03T01:39:47Z-
dc.date.issued2022-
dc.identifier.citationYang, D. J. (2022). IOT elderly fall detector. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/162670en_US
dc.identifier.urihttps://hdl.handle.net/10356/162670-
dc.description.abstractThe purpose of this report investigates and implement different state-of-the-art deep learning network models for the elderly fall detector CCTV. We will be using 2 models to detect the humans pose estimation in the CCTV video. The first model that we will be using is a pretrain faster RCNN model which is a deep convolutional neural network used for object detection. This model is developed by a group of Microsoft Research. The faster RCNN model can identify the locations of different objects precisely and quickly. We will be using this model to detect the human object in the CCTV camera. The second model that we will be using is Resnet 50 which was develop in 2015 by Kaiming He et al from MS research team. This model won the ImageNet competition in 2015. This model proves that very deep network layer can work too. In this project, we will use the transfer learning method with the backbone of pretrain Resnet50 model to train the data and use heatmap to predict the pose estimation of human joints using the COCO dataset annotation of 17 human joints key point.en_US
dc.language.isoenen_US
dc.publisherNanyang Technological Universityen_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.titleIOT elderly fall detectoren_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorDusit Niyatoen_US
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.description.degreeBachelor of Engineering (Computer Science)en_US
dc.contributor.supervisoremailDNIYATO@ntu.edu.sgen_US
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Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)
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