Long-term clothes-changing person re-identification
Person Re-Identification(Person ReID) focuses on the searching and identifica- tion of pedestrians in cross-camera scenery. It can be regarded as a signif- icant complement to face recognition, providing sufficient features when face information cannot be obtained. Long Term Person Reid is a challen...
Saved in:
Main Author: | |
---|---|
Other Authors: | |
Format: | Thesis-Master by Coursework |
Language: | English |
Published: |
Nanyang Technological University
2022
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/158908 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
id |
sg-ntu-dr.10356-158908 |
---|---|
record_format |
dspace |
spelling |
sg-ntu-dr.10356-1589082023-07-04T17:51:51Z Long-term clothes-changing person re-identification Lu, Moyang Alex Chichung Kot School of Electrical and Electronic Engineering Rapid-Rich Object Search (ROSE) Lab EACKOT@ntu.edu.sg Engineering::Electrical and electronic engineering::Electronic systems::Signal processing Person Re-Identification(Person ReID) focuses on the searching and identifica- tion of pedestrians in cross-camera scenery. It can be regarded as a signif- icant complement to face recognition, providing sufficient features when face information cannot be obtained. Long Term Person Reid is a challenging sub- question that aims to match the same target for a long duration, and because it is a long-term problem, the individuals could be captured wearing different clothes. We assume that the face information is not accessible, because in the surveillance image, the faces are always very blurry or occluded. This disser- tation explores the performance of current representative methods on different datasets and sub-datasets, including more than 80 comparative experiments to compare the performance between using the human scale, gait features, seman- tically guided methods and ResNet50 baseline for comparison. In addition, dif- ferent datasets are also used for testing, including pure clothes changing datasets and hybrid datasets as well as different sub-datasets to test the performance of various methods. At the same time, based on the structure of ResNet50, this dissertation tries to add a mask structure to the building block, hoping to im- prove the model’s ability to perceive specific areas. In addition, for the semantic guidance method with better effect, this dissertation obtained a series of bench- mark results under different datasets. And it tested the influence of different data processing methods on the experimental results. Improvements were made and many meaningful and groundbreaking conclusions were drawn. Master of Science (Signal Processing) 2022-06-02T05:36:55Z 2022-06-02T05:36:55Z 2022 Thesis-Master by Coursework Lu, M. (2022). Long-term clothes-changing person re-identification. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/158908 https://hdl.handle.net/10356/158908 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 |
Engineering::Electrical and electronic engineering::Electronic systems::Signal processing |
spellingShingle |
Engineering::Electrical and electronic engineering::Electronic systems::Signal processing Lu, Moyang Long-term clothes-changing person re-identification |
description |
Person Re-Identification(Person ReID) focuses on the searching and identifica- tion of pedestrians in cross-camera scenery. It can be regarded as a signif- icant complement to face recognition, providing sufficient features when face information cannot be obtained. Long Term Person Reid is a challenging sub- question that aims to match the same target for a long duration, and because it is a long-term problem, the individuals could be captured wearing different clothes. We assume that the face information is not accessible, because in the surveillance image, the faces are always very blurry or occluded. This disser- tation explores the performance of current representative methods on different datasets and sub-datasets, including more than 80 comparative experiments to compare the performance between using the human scale, gait features, seman- tically guided methods and ResNet50 baseline for comparison. In addition, dif- ferent datasets are also used for testing, including pure clothes changing datasets and hybrid datasets as well as different sub-datasets to test the performance of various methods. At the same time, based on the structure of ResNet50, this dissertation tries to add a mask structure to the building block, hoping to im- prove the model’s ability to perceive specific areas. In addition, for the semantic guidance method with better effect, this dissertation obtained a series of bench- mark results under different datasets. And it tested the influence of different data processing methods on the experimental results. Improvements were made and many meaningful and groundbreaking conclusions were drawn. |
author2 |
Alex Chichung Kot |
author_facet |
Alex Chichung Kot Lu, Moyang |
format |
Thesis-Master by Coursework |
author |
Lu, Moyang |
author_sort |
Lu, Moyang |
title |
Long-term clothes-changing person re-identification |
title_short |
Long-term clothes-changing person re-identification |
title_full |
Long-term clothes-changing person re-identification |
title_fullStr |
Long-term clothes-changing person re-identification |
title_full_unstemmed |
Long-term clothes-changing person re-identification |
title_sort |
long-term clothes-changing person re-identification |
publisher |
Nanyang Technological University |
publishDate |
2022 |
url |
https://hdl.handle.net/10356/158908 |
_version_ |
1772827616605110272 |