Please use this identifier to cite or link to this item:
Full metadata record
DC FieldValueLanguage
dc.contributor.authorMeng, Zexinen_US
dc.identifier.citationMeng, Z. (2022). Person re-identification for similar clothing or uniform problem. Master's thesis, Nanyang Technological University, Singapore.
dc.description.abstractPerson re-identification (Re-ID) is a technique to retrieve the specific pedestrian in an image or video sequence based on general characteristics of the human body. The information on pedestrian clothing has a significant impact on person Re-ID results. However, in some particular cases (e.g., hospitals, schools, and teams, where everyone must dress uniformly), the clothing features of the target person in the query dataset and other pedestrians in the gallery are almost identical. This brings many challenges to the current person Re-ID methods challenging under similar clothing scenarios. To improve the accuracy in this scenario, this dissertation contrasts the semantic segmentation model with the head-shoulder adaptive attention network (HAA), and the most robust semantic segmentation model was found for the uniform class dataset. Finally, the model has extensively experimented on the datasets Black-reID and NTUOutdoors, with Rank1 significantly outperforming earlier techniques.en_US
dc.publisherNanyang Technological Universityen_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.titlePerson re-identification for similar clothing or uniform problemen_US
dc.typeThesis-Master by Courseworken_US
dc.contributor.supervisorAlex Chichung Koten_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeMaster of Science (Signal Processing)en_US
item.fulltextWith Fulltext-
Appears in Collections:EEE Theses
Files in This Item:
File Description SizeFormat 
  Restricted Access
5.36 MBAdobe PDFView/Open

Page view(s)

Updated on May 19, 2024


Updated on May 19, 2024

Google ScholarTM


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