Deep learning for unmanned aerial vehicle re-identification

Recently, Unmanned Aerial Vehicle (UAV) Re-Identification (ReID) has gained significant popularity in computer vision and ReID. This emerging technology holds considerable significance in urban security and military applications. In contrast to traditional ReID methods that depend on stationary came...

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Main Author: Wang, Zekai
Other Authors: Yap Kim Hui
Format: Thesis-Master by Coursework
Language:English
Published: Nanyang Technological University 2024
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Online Access:https://hdl.handle.net/10356/173434
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1734342024-02-09T15:42:17Z Deep learning for unmanned aerial vehicle re-identification Wang, Zekai Yap Kim Hui School of Electrical and Electronic Engineering EKHYAP@NTU.EDU.SG Computer and Information Science Recently, Unmanned Aerial Vehicle (UAV) Re-Identification (ReID) has gained significant popularity in computer vision and ReID. This emerging technology holds considerable significance in urban security and military applications. In contrast to traditional ReID methods that depend on stationary cameras with fixed perspectives, UAV-based ReID encounters more challenging obstacles. UAVs’ flexible and movable viewpoints introduce more appearance ambiguities among objects, making ReID more challenging. Moreover, the computing and power limitation of UAV platforms poses constraints on the use of large-scale deep learning networks. The above factors result in the inherent difficulty in achieving high accuracy in UAV-based ReID research. To solve these problems, this dissertation employs three baselines from FastReID to conduct experiments in two scenarios: UAV-based person ReID and vehicle ReID. After comparison, the Stronger Baseline (SBS) model with ResNeSt as backbone network and Instance and Batch Normalization (IBN) performed best for UAV-based person and vehicle ReID, which reduced processing time and computing costs. Through detailed and comprehensive experiments and in-depth analysis of the results, valuable insights are provided for future studies on UAV-based ReID. Master's degree 2024-02-05T02:44:03Z 2024-02-05T02:44:03Z 2023 Thesis-Master by Coursework Wang, Z. (2023). Deep learning for unmanned aerial vehicle re-identification. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/173434 https://hdl.handle.net/10356/173434 en application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Computer and Information Science
spellingShingle Computer and Information Science
Wang, Zekai
Deep learning for unmanned aerial vehicle re-identification
description Recently, Unmanned Aerial Vehicle (UAV) Re-Identification (ReID) has gained significant popularity in computer vision and ReID. This emerging technology holds considerable significance in urban security and military applications. In contrast to traditional ReID methods that depend on stationary cameras with fixed perspectives, UAV-based ReID encounters more challenging obstacles. UAVs’ flexible and movable viewpoints introduce more appearance ambiguities among objects, making ReID more challenging. Moreover, the computing and power limitation of UAV platforms poses constraints on the use of large-scale deep learning networks. The above factors result in the inherent difficulty in achieving high accuracy in UAV-based ReID research. To solve these problems, this dissertation employs three baselines from FastReID to conduct experiments in two scenarios: UAV-based person ReID and vehicle ReID. After comparison, the Stronger Baseline (SBS) model with ResNeSt as backbone network and Instance and Batch Normalization (IBN) performed best for UAV-based person and vehicle ReID, which reduced processing time and computing costs. Through detailed and comprehensive experiments and in-depth analysis of the results, valuable insights are provided for future studies on UAV-based ReID.
author2 Yap Kim Hui
author_facet Yap Kim Hui
Wang, Zekai
format Thesis-Master by Coursework
author Wang, Zekai
author_sort Wang, Zekai
title Deep learning for unmanned aerial vehicle re-identification
title_short Deep learning for unmanned aerial vehicle re-identification
title_full Deep learning for unmanned aerial vehicle re-identification
title_fullStr Deep learning for unmanned aerial vehicle re-identification
title_full_unstemmed Deep learning for unmanned aerial vehicle re-identification
title_sort deep learning for unmanned aerial vehicle re-identification
publisher Nanyang Technological University
publishDate 2024
url https://hdl.handle.net/10356/173434
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