Comparison of different optical flow methods on optical flow based out-of-distribution detection

In Cyber Physical Systems (CPS), object detection is a crucial aspect that ensures the safe and robust autonomous operation of such systems. However, studies have suggested that these object detection algorithms are susceptible to anomalies that are falsely classified. Consequently, there is a need...

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Main Author: Chua, Ivan Hao En
Other Authors: Arvind Easwaran
Format: Final Year Project (FYP)
Language:English
Published: Nanyang Technological University 2022
Subjects:
Online Access:https://hdl.handle.net/10356/162920
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author Chua, Ivan Hao En
author2 Arvind Easwaran
author_facet Arvind Easwaran
Chua, Ivan Hao En
author_sort Chua, Ivan Hao En
collection NTU
description In Cyber Physical Systems (CPS), object detection is a crucial aspect that ensures the safe and robust autonomous operation of such systems. However, studies have suggested that these object detection algorithms are susceptible to anomalies that are falsely classified. Consequently, there is a need to develop high-performing Out-of- Distrubution (OOD) detection algorithms for such systems. The use of optical flow as a motion detection algorithm has historically been well- documented, and works have been done to incorporate optical flow into Variational Autoencoders (VAEs). This project proposes another method of OOD detection by incorporating the Lucas-Kanade model into a VAE. This proposed model is compared against an existing optical flow-basedd VAE that was implemented with the Farneback algorithm, and the performance and accuracy of both models were tested with a dataset that was self-collected.
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spelling ntu-10356/1629202022-11-14T03:49:01Z Comparison of different optical flow methods on optical flow based out-of-distribution detection Chua, Ivan Hao En Arvind Easwaran School of Computer Science and Engineering arvinde@ntu.edu.sg Engineering::Computer science and engineering In Cyber Physical Systems (CPS), object detection is a crucial aspect that ensures the safe and robust autonomous operation of such systems. However, studies have suggested that these object detection algorithms are susceptible to anomalies that are falsely classified. Consequently, there is a need to develop high-performing Out-of- Distrubution (OOD) detection algorithms for such systems. The use of optical flow as a motion detection algorithm has historically been well- documented, and works have been done to incorporate optical flow into Variational Autoencoders (VAEs). This project proposes another method of OOD detection by incorporating the Lucas-Kanade model into a VAE. This proposed model is compared against an existing optical flow-basedd VAE that was implemented with the Farneback algorithm, and the performance and accuracy of both models were tested with a dataset that was self-collected. Bachelor of Engineering (Computer Science) 2022-11-14T03:49:01Z 2022-11-14T03:49:01Z 2022 Final Year Project (FYP) Chua, I. H. E. (2022). Comparison of different optical flow methods on optical flow based out-of-distribution detection. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/162920 https://hdl.handle.net/10356/162920 en SCSE21-0864 application/pdf Nanyang Technological University
spellingShingle Engineering::Computer science and engineering
Chua, Ivan Hao En
Comparison of different optical flow methods on optical flow based out-of-distribution detection
title Comparison of different optical flow methods on optical flow based out-of-distribution detection
title_full Comparison of different optical flow methods on optical flow based out-of-distribution detection
title_fullStr Comparison of different optical flow methods on optical flow based out-of-distribution detection
title_full_unstemmed Comparison of different optical flow methods on optical flow based out-of-distribution detection
title_short Comparison of different optical flow methods on optical flow based out-of-distribution detection
title_sort comparison of different optical flow methods on optical flow based out of distribution detection
topic Engineering::Computer science and engineering
url https://hdl.handle.net/10356/162920
work_keys_str_mv AT chuaivanhaoen comparisonofdifferentopticalflowmethodsonopticalflowbasedoutofdistributiondetection