Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/157277
Title: Car cabin surveillance using computer vision
Authors: Soegeng, Andrew Ivan
Keywords: Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Soegeng, A. I. (2022). Car cabin surveillance using computer vision. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/157277
Project: B3039-211
Abstract: Driver assistance systems (DAS) in cars has become more intelligent than ever and it has enabled autonomous driving in certain conditions. Consequently, there is a need to always monitor the drivers’ attention inside the car to make sure that they are attentive enough and ready to take over when the DAS asked. As such, this research proposes a new computer vision approach of where there would be a wide-angle camera placed in the center mirror monitoring the driver and passenger and it will be connected to a computing unit inside the car which utilizes deep learning-based AI model to detect the activity of the driver and passenger and determine whether it is safe to engage level 2 DAS. In this paper, a literature review about the different approaches to the problem will be given together with the different datasets that are available to support the model training. Moreover, this paper presents the findings and the results from 2 different approaches which are classic image classification-based approach, and a novel 2-stage classifier was proposed whereby the frames are passed to pose estimation and face mesh detection model that will output key points for both the face mesh and the human body which are then passed to an algorithm that will output the driver activity.
URI: https://hdl.handle.net/10356/157277
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

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