Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/140370
Title: Human-centric AI security
Authors: Ling, Shahrul Al-Nizam
Keywords: Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
Engineering::Electrical and electronic engineering
Issue Date: 2020
Publisher: Nanyang Technological University
Project: B3279-191
Abstract: In the field of computer vision, the use of machine learning methods for security involves detection and recognition. When used in conjunction with surveillance, one can enhance the safety it provides. Through human action recognition, unsavoury behaviour can be detected which provides greater peace of mind for the public. In this project, the author created a baseline action recognition framework. Starting with the building of a custom dataset from Closed-Circuit Television (CCTV) footage of an office space. This custom dataset is created with an action recognition model using the human skeletal structures in mind. Therefore, the custom dataset is to only retain videos that contain said structures in order to minimize costs incurred when it is to be sent for manual labelling. Tracking of these skeletal structures is also done in order to properly label the relevant actions recognized with the person doing said action. This is done by repurposing a person re-identification (ReID) framework for tracking of a person within the video. Action recognition is then done using a Spatial-Temporal Graph Convolutional Neural Network (ST-GCN). As an initial test of this framework, the available action classes that were labeled in-house are ‘Running’, ‘Walking’, ‘Standing’ and ‘Sitting’.
URI: https://hdl.handle.net/10356/140370
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

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