Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/163987
Title: Person re-identification for similar clothing or uniform problem
Authors: Meng, Zexin
Keywords: Engineering::Computer science and engineering
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Meng, Z. (2022). Person re-identification for similar clothing or uniform problem. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/163987
Abstract: Person 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.
URI: https://hdl.handle.net/10356/163987
Schools: School of Electrical and Electronic Engineering 
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

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