Re-identification with Recurrent Neural Networks
(2017) In Master's Theses in Mathematical Sciences FMA820 20171Mathematics (Faculty of Engineering)
- Abstract
- Today re-identification of persons in disjoint camera views demand large human effort and resources. There is a need of autonomous re-identification systems.
This thesis has been focused on the development of autonomous re-identification systems based on recurrent neural networks able to analyze sequences of images of persons, enabling the possibility to filter out outliers within sequences and make use of spatio-temporal features. Through the development and evaluation of multiple network architectures and recurrent units a broad examination of the viability of recurrent neural networks in re-identification systems have been made.
This thesis has shown, analysis of sequences of images can significantly increase the performance of... (More) - Today re-identification of persons in disjoint camera views demand large human effort and resources. There is a need of autonomous re-identification systems.
This thesis has been focused on the development of autonomous re-identification systems based on recurrent neural networks able to analyze sequences of images of persons, enabling the possibility to filter out outliers within sequences and make use of spatio-temporal features. Through the development and evaluation of multiple network architectures and recurrent units a broad examination of the viability of recurrent neural networks in re-identification systems have been made.
This thesis has shown, analysis of sequences of images can significantly increase the performance of re-identification systems compared to analysis of single images, with a doubling of the re-identification performance in multiple cases. Non-dynamic features tend to dominate the heap of information from sequences used during re-identification.
A realistic benchmark dataset was created as part of the thesis. Evaluation on the benchmark dataset has shown, to achieve satisfactory performance in real-world scenarios of autonomous re-identification systems, based on recurrent neural networks, larger training datasets with greater variety are needed. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/8925200
- author
- Skog, Johannes LU
- supervisor
- organization
- course
- FMA820 20171
- year
- 2017
- type
- H2 - Master's Degree (Two Years)
- subject
- publication/series
- Master's Theses in Mathematical Sciences
- report number
- LUTFMA-3328-2017
- ISSN
- 1404-6342
- other publication id
- 2017:E54
- language
- English
- id
- 8925200
- date added to LUP
- 2017-09-19 16:02:11
- date last changed
- 2017-09-19 16:02:11
@misc{8925200,
abstract = {{Today re-identification of persons in disjoint camera views demand large human effort and resources. There is a need of autonomous re-identification systems.
This thesis has been focused on the development of autonomous re-identification systems based on recurrent neural networks able to analyze sequences of images of persons, enabling the possibility to filter out outliers within sequences and make use of spatio-temporal features. Through the development and evaluation of multiple network architectures and recurrent units a broad examination of the viability of recurrent neural networks in re-identification systems have been made.
This thesis has shown, analysis of sequences of images can significantly increase the performance of re-identification systems compared to analysis of single images, with a doubling of the re-identification performance in multiple cases. Non-dynamic features tend to dominate the heap of information from sequences used during re-identification.
A realistic benchmark dataset was created as part of the thesis. Evaluation on the benchmark dataset has shown, to achieve satisfactory performance in real-world scenarios of autonomous re-identification systems, based on recurrent neural networks, larger training datasets with greater variety are needed.}},
author = {{Skog, Johannes}},
issn = {{1404-6342}},
language = {{eng}},
note = {{Student Paper}},
series = {{Master's Theses in Mathematical Sciences}},
title = {{Re-identification with Recurrent Neural Networks}},
year = {{2017}},
}