Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/150048
Title: AI edge device based human action recognition for mobile platforms
Authors: Yap, Winnchis
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2021
Publisher: Nanyang Technological University
Source: Yap, W. (2021). AI edge device based human action recognition for mobile platforms. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/150048
Project: A3225-201
Abstract: Deep learning has contributed to a huge improvement to the ever-developing field of Computer Vision. Many state-of-the-art applications like face recognition or machine vision in self-driving cars have been introduced based on Computer Vision. In real world cases, we find that an activity does not occur alone by itself but as an interaction between people and objects. Humans are able to distinguish such actions as we are able to associate them with our ability to apply logic and reason. However, a computer does not have such association capabilities. With the fast advancements in Computer Vision, there are also discussions about privacy concerns due to the large datasets necessary for deep learning. These datasets may contain sensitive information which is prone to vulnerabilities when processed on the cloud. In this paper, we design an activity recognition model by combining an action recognition and object detection model together and implement it on an edge device to tackle these issues. We will also discuss on the key findings and the challenges and future work that could be done.
URI: https://hdl.handle.net/10356/150048
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

Files in This Item:
File Description SizeFormat 
Yap Winnchis Final Report U1720784F.pdf
  Restricted Access
3.08 MBAdobe PDFView/Open

Page view(s)

115
Updated on Dec 5, 2022

Download(s)

9
Updated on Dec 5, 2022

Google ScholarTM

Check

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.