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Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.
Person Re-Identification System at Semantic Level based on Pedestrian Attributes Ontology
Digital Object Identifier (DOI) : 10.14569/IJACSA.2020.0110264
Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 2, 2020.
Abstract: Person Re-Identification (Re-ID) is a very important task in video surveillance systems such as tracking people, finding people in public places, or analysing customer behavior in supermarkets. Although there have been many works to solve this problem, there are still remaining challenges such as large-scale datasets, imbalanced data, viewpoint, fine-grained data (attributes), the Local Features are not employed at semantic level in online stage of Re-ID task, furthermore, the imbalanced data problem of attributes are not taken into consideration. This paper has proposed a Unified Re-ID system consisted of three main modules such as Pedestrian Attribute Ontology (PAO), Local Multi-task DCNN (Local MDCNN), Imbalance Data Solver (IDS). The new main point of our Re-ID system is the power of mutual support of PAO, Local MDCNN and IDS to exploit the inner-group correlations of attributes and pre-filter the mismatch candidates from Gallery set based on semantic information as Fashion Attributes and Facial Attributes, to solve the imbalanced data of attributes without adjusting network architecture and data augmentation. We experimented on the well-known Market1501 dataset. The experimental results have shown the effectiveness of our Re-ID system and it could achieve the higher performance on Market1501 dataset in comparison to some state-of-the-art Re-ID methods.
Keywords: Person Re-Identification (Re-ID); Pedestrian Attributes Ontology (PAO); Deep Convolution Neuron Network (DCNN); Multi-task Deep Convolution Neuron Network (MDCNN); Local Multi-task Deep Convolution Neuron Network (Local MDCNN); Imbalanced Data Solver (IDS
Ngoc Q. Ly, Hieu N. M. Cao and Thi T. Nguyen, “Person Re-Identification System at Semantic Level based on Pedestrian Attributes Ontology” International Journal of Advanced Computer Science and Applications(IJACSA), 11(2), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110264
@article{Ly2020,
title = {Person Re-Identification System at Semantic Level based on Pedestrian Attributes Ontology},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110264},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110264},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {2},
author = {Ngoc Q. Ly and Hieu N. M. Cao and Thi T. Nguyen}
}
