MARC details
| 000 -LEADER |
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| fixed length control field |
02721ntm a2200337 i 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
MY-KuUP |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20251125110900.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS |
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| fixed length control field |
t||||fr|||| 00| 0 |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
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| fixed length control field |
ta |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
240529t20222022my a|||fs|||| 000 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
THE0009861 (Local) |
| Qualifying information |
Hardback |
| 040 ## - CATALOGING SOURCE |
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| Original cataloging agency |
UMPSA |
| Language of cataloging |
eng |
| Transcribing agency |
UMP |
| Description conventions |
rda |
| 090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN) |
|---|
| Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) |
PSM .T43 2022 r Bc |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Thachayane Radhakrishnan, |
| Relator term |
author. |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Comparative study of person re-identification using deep learning approaches / |
| Statement of responsibility, etc. |
Thachayane Radhakrishnan |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Place of production, publication, distribution, manufacture |
Kuantan, Pahang : |
| Name of producer, publisher, distributor, manufacturer |
UMPSA, |
| Date of production, publication, distribution, manufacture, or copyright notice |
2022 |
| 264 #4 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Date of production, publication, distribution, manufacture, or copyright notice |
©2022 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xi, 135 pages : |
| Other physical details |
illustrations (some color) ; |
| Dimensions |
30 cm. + |
| Accompanying material |
1 CD-ROM |
| 336 ## - CONTENT TYPE |
|---|
| Source |
rdacontent |
| Content type term |
text |
| 337 ## - MEDIA TYPE |
|---|
| Source |
rdamedia |
| Media type term |
unmediated |
| 338 ## - CARRIER TYPE |
|---|
| Source |
rdacarrier |
| Carrier type term |
volume |
| 347 ## - DIGITAL FILE CHARACTERISTICS |
|---|
| Source |
rda |
| File type |
text file |
| Encoding format |
PDF |
| 500 ## - GENERAL NOTE |
|---|
| General note |
Centre for Mathematical Sciences |
| 502 ## - DISSERTATION NOTE |
|---|
| Dissertation note |
Bachelor of Applied Science in Data Analytics with Honours-- Universiti Malaysia Pahang – 2022 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc. note |
Includes bibliographical references |
| 520 3# - SUMMARY, ETC. |
|---|
| Summary, etc. |
Person Re-Identification is important in video tracking applications and is still a work in progress to date. Person Re-ID is useful in real-time criminal investigation to re-identify a criminal or accused person to overcome security measures. In this work, the concentration is on Image-to-Video Person Re-ID, which relates a certain probe image to videos in the gallery. It has important applications in tracing the position of a lost individual or criminal tracking in real-time despite all the limitations such as low resolution, occlusion, angle and pose variance, illumination change, and heterogeneous matching. There are more than 100 of methods are in use today for Person Re-ID. In this project, a few deep learning approaches will be studied and compared known as Reciprocal Attention Discriminator (READ), Attention loss paired with OSM loss and CL Centers and Temporal Knowledge Propagation (TKP). These methods will be applied to the Person Re-ID setting. The effectiveness of the proposed framework was analysed using state-of-the-art Person Re-ID approaches as mentioned above on the Motian Analysis and Re-Identification Set (MARS), a benchmark dataset. The results were visualized by using Tableau software. From the results, Attention loss paired with OSM loss and CL Centers achieved high mean Average Precision (mAP) which is 82.9% compared to READ and TKP. This study contributes significantly to the wide application of re-identification systems in realistic real-life scenarios. |
| 610 20 - SUBJECT ADDED ENTRY--CORPORATE NAME |
|---|
| Corporate name or jurisdiction name as entry element |
Centre for Mathematical Sciences |
| General subdivision |
Dissertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
Universities and colleges |
| General subdivision |
Dissertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Final Year Project |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Library of Congress Classification |
| Koha item type |
Final Year Report |