Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/154813
Title: Vehicle re-identification using machine learning
Authors: Tang, Lisha
Keywords: Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Issue Date: 2021
Publisher: Nanyang Technological University
Source: Tang, L. (2021). Vehicle re-identification using machine learning. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/154813
Abstract: Vehicle Re-ID aims to retrieve images of the same vehicle across non-overlapping cameras. The key challenges lie in the subtle inter-class discrepancy caused by near-duplicated identities and the significant intra-class distance due to diverse factors, including illumination, viewpoints, and background interference. This thesis starts with reviewing the development history of vehicle Re-ID and proposes a Part-Mentored Attention Network (PMANet) consisting of a Part Attention Network (PANet) for weakly-supervised vehicle part localization and a Part-Mentored Network (PMNet) for mentoring the global and local feature aggregation. Firstly, PANet predicts a foreground mask and pinpoints K prominent vehicle parts without additional part-level supervision. Secondly, PMNet applies multi-scale soft attention on localized regions and compensates inaccurate part masks with part-guided learning. PANet and PMNet construct a two-stage attention structure to perform a coarse-to-fine search among identities. Finally, we address this Re-ID issue as a multi-task problem and employ Homoscedastic Uncertainty Learning to automatically balance the loss weightings. Experimental results show that our approach outperforms recent state-of-the-art methods by averagely 2.63% in CMC@1 on VehicleID and 2.2% in mAP on VeRi776.
URI: https://hdl.handle.net/10356/154813
DOI: 10.32657/10356/154813
Rights: This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

Files in This Item:
File Description SizeFormat 
MEng_Thesis_TangLisha.pdf3.46 MBAdobe PDFView/Open

Page view(s)

112
Updated on Jun 26, 2022

Download(s) 20

167
Updated on Jun 26, 2022

Google ScholarTM

Check

Altmetric


Plumx

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