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Multi-Modal Long-Term Person Re-Identification Using Physical Soft Bio-Metrics and Body Figure

Shoukry, Nadeen ; Abd El Ghany, Mohamed A. ; Salem, Mohammed A.-M. (2022)
Multi-Modal Long-Term Person Re-Identification Using Physical Soft Bio-Metrics and Body Figure.
In: Applied Sciences, 12 (6)
doi: 10.3390/app12062835
Artikel, Bibliographie

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Kurzbeschreibung (Abstract)

Person re-identification is the task of recognizing a subject across different non-overlapping cameras across different views and times. Most state-of-the-art datasets and proposed solutions tend to address the problem of short-term re-identification. Those models can re-identify a person as long as they are wearing the same clothes. The work presented in this paper addresses the task of long-term re-identification. Therefore, the proposed model is trained on a dataset that incorporates clothes variation. This paper proposes a multi-modal person re-identification model. The first modality includes soft bio-metrics: hair, face, neck, shoulders, and part of the chest. The second modality is the remaining body figure that mainly focuses on clothes. The proposed model is composed of two separate neural networks, one for each modality. For the first modality, a two-stream Siamese network with pre-trained FaceNet as a feature extractor for the first modality is utilized. Part-based Convolutional Baseline classifier with a feature extractor network OSNet for the second modality. Experiments confirm that the proposed model can outperform several state-of-the-art models achieving 81.4 % accuracy on Rank-1, 82.3% accuracy on Rank-5, 83.1% accuracy on Rank-10, and 83.7% accuracy on Rank-20.

Typ des Eintrags: Artikel
Erschienen: 2022
Autor(en): Shoukry, Nadeen ; Abd El Ghany, Mohamed A. ; Salem, Mohammed A.-M.
Art des Eintrags: Bibliographie
Titel: Multi-Modal Long-Term Person Re-Identification Using Physical Soft Bio-Metrics and Body Figure
Sprache: Englisch
Publikationsjahr: 2022
Verlag: MDPI
Titel der Zeitschrift, Zeitung oder Schriftenreihe: Applied Sciences
Jahrgang/Volume einer Zeitschrift: 12
(Heft-)Nummer: 6
Kollation: 18 Seiten
DOI: 10.3390/app12062835
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Kurzbeschreibung (Abstract):

Person re-identification is the task of recognizing a subject across different non-overlapping cameras across different views and times. Most state-of-the-art datasets and proposed solutions tend to address the problem of short-term re-identification. Those models can re-identify a person as long as they are wearing the same clothes. The work presented in this paper addresses the task of long-term re-identification. Therefore, the proposed model is trained on a dataset that incorporates clothes variation. This paper proposes a multi-modal person re-identification model. The first modality includes soft bio-metrics: hair, face, neck, shoulders, and part of the chest. The second modality is the remaining body figure that mainly focuses on clothes. The proposed model is composed of two separate neural networks, one for each modality. For the first modality, a two-stream Siamese network with pre-trained FaceNet as a feature extractor for the first modality is utilized. Part-based Convolutional Baseline classifier with a feature extractor network OSNet for the second modality. Experiments confirm that the proposed model can outperform several state-of-the-art models achieving 81.4 % accuracy on Rank-1, 82.3% accuracy on Rank-5, 83.1% accuracy on Rank-10, and 83.7% accuracy on Rank-20.

Freie Schlagworte: FaceNet, long-term person re-identification, OSNet, PCB, PRCC dataset, Siamese network
Sachgruppe der Dewey Dezimalklassifikatin (DDC): 600 Technik, Medizin, angewandte Wissenschaften > 620 Ingenieurwissenschaften und Maschinenbau
Fachbereich(e)/-gebiet(e): 18 Fachbereich Elektrotechnik und Informationstechnik
18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Datentechnik
18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Datentechnik > Integrierte Elektronische Systeme (IES)
Hinterlegungsdatum: 02 Aug 2024 12:39
Letzte Änderung: 02 Aug 2024 12:39
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