Budiatmadjaja, William and Rusyadi, Rusman and Steinhage, Axel (2020) Design and Construction of an Indoor People Counting System using SensFloor® and Computational Neural Network. Bachelor thesis, Swiss German University.
|
Text
William Budiatmadjaja 11601018-TOC.pdf Download (1MB) | Preview |
|
![[img]](http://repository.sgu.ac.id/style/images/fileicons/text.png) |
Text
William Budiatmadjaja 11601018-1.pdf Restricted to Registered users only Download (417kB) |
|
|
Text
William Budiatmadjaja 11601018-2.pdf Restricted to Registered users only Download (2MB) |
|
|
Text
William Budiatmadjaja 11601018-3.pdf Restricted to Registered users only Download (1MB) |
|
|
Text
William Budiatmadjaja 11601018-4.pdf Restricted to Registered users only Download (6MB) |
|
|
Text
William Budiatmadjaja 11601018-5.pdf Restricted to Registered users only Download (226kB) |
|
|
Text
William Budiatmadjaja 11601018-Ref.pdf Download (641kB) | Preview |
Abstract
Due to the high need for ambient assisted living, the thesis presents methods for human sensing and counting using the SensFloor system. The usage of neural network architectures such as feed-forward, recurrent, and convolution neural network is done to see which architectures perform the best. The dataset taken from data logs and virtually generated are used to train the model using the TensorFlow library. Performing similarly, recurrent and convolution neural networks are researched further to sense and count humans through coordinate grid segmentation. The usage of a proper neural network algorithm will increase the performance of the SensFloor system, as proven on the single object localisation problem.
| Item Type: | Thesis (Bachelor) |
|---|---|
| Uncontrolled Keywords: | SensFloor; Ambient Assisted Living; Machine Learning; Recurrent Neural Networks; Convolution Neural Networks; Object Detection; TensorFlow |
| Subjects: | Q Science > QA Mathematics > QA76 Computer software Q Science > QA Mathematics > QA76 Computer software > QA76.87 Neural networks (Computer science) T Technology > TJ Mechanical engineering and machinery > TJ163.12 Mechatronics |
| Divisions: | Faculty of Engineering and Information Technology > Department of Mechatronics Engineering |
| Depositing User: | Faisal Ifzaldi |
| Date Deposited: | 02 Nov 2020 14:09 |
| Last Modified: | 02 Nov 2020 14:09 |
| URI: | http://repository.sgu.ac.id/id/eprint/1927 |
Actions (login required)
 |
View Item |