Multimodal Neural Network for Overhead Person Re-identification

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Resumé

Person re-identification is a topic which has potential to be used for applications within forensics, flow analysis and queue monitoring. It is the process of matching persons across two or more camera views, most often by extracting colour and texture based hand-crafted features, to identify similar persons. Because of challenges regarding changes in lighting between views, occlusion
or even privacy issues, more focus has turned to overhead and depth based camera solutions. Therefore, we have developed a system, based on a Convolutional Neural Network (CNN) which is trained using both depth and RGB modalities to provide a fused feature. By training on a locally collected dataset, we achieve a rank-1 accuracy of 74.69%, increased by 16.00% compared to using a single modality. Furthermore, tests on two similar publicly available benchmark datasets of TVPR and DPI-T show accuracies of 77.66% and 90.36%, respectively, outperforming state-of-the-art results by 3.60% and 5.20%, respectively.
OriginalsprogEngelsk
Titel16th International Conference of the Biometrics Special Interest Group
ForlagIEEE
Publikationsdato2017
ISBN (Trykt)978-1-5386-0396-3
ISBN (Elektronisk)978-3-88579-664-0
DOI
StatusUdgivet - 2017
Begivenhed16th International Conference of the Biometrics Special Interest Group - Darmstadt, Tyskland
Varighed: 20 sep. 201722 sep. 2017

Konference

Konference16th International Conference of the Biometrics Special Interest Group
LandTyskland
ByDarmstadt
Periode20/09/201722/09/2017
NavnLecture Notes in Informatics
Vol/bind2017
ISSN1617-5468

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Cameras
Neural networks
Textures
Lighting
Color
Monitoring

Citer dette

Lejbølle, A. R., Nasrollahi, K., Krogh, B., & Moeslund, T. B. (2017). Multimodal Neural Network for Overhead Person Re-identification. I 16th International Conference of the Biometrics Special Interest Group IEEE. Lecture Notes in Informatics, Bind. 2017 https://doi.org/10.23919/BIOSIG.2017.8053514
Lejbølle, Aske Rasch ; Nasrollahi, Kamal ; Krogh, Benjamin ; Moeslund, Thomas B. / Multimodal Neural Network for Overhead Person Re-identification. 16th International Conference of the Biometrics Special Interest Group. IEEE, 2017. (Lecture Notes in Informatics, Bind 2017).
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title = "Multimodal Neural Network for Overhead Person Re-identification",
abstract = "Person re-identification is a topic which has potential to be used for applications within forensics, flow analysis and queue monitoring. It is the process of matching persons across two or more camera views, most often by extracting colour and texture based hand-crafted features, to identify similar persons. Because of challenges regarding changes in lighting between views, occlusionor even privacy issues, more focus has turned to overhead and depth based camera solutions. Therefore, we have developed a system, based on a Convolutional Neural Network (CNN) which is trained using both depth and RGB modalities to provide a fused feature. By training on a locally collected dataset, we achieve a rank-1 accuracy of 74.69{\%}, increased by 16.00{\%} compared to using a single modality. Furthermore, tests on two similar publicly available benchmark datasets of TVPR and DPI-T show accuracies of 77.66{\%} and 90.36{\%}, respectively, outperforming state-of-the-art results by 3.60{\%} and 5.20{\%}, respectively.",
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Lejbølle, AR, Nasrollahi, K, Krogh, B & Moeslund, TB 2017, Multimodal Neural Network for Overhead Person Re-identification. i 16th International Conference of the Biometrics Special Interest Group. IEEE, Lecture Notes in Informatics, bind 2017, 16th International Conference of the Biometrics Special Interest Group, Darmstadt, Tyskland, 20/09/2017. https://doi.org/10.23919/BIOSIG.2017.8053514

Multimodal Neural Network for Overhead Person Re-identification. / Lejbølle, Aske Rasch; Nasrollahi, Kamal; Krogh, Benjamin; Moeslund, Thomas B.

16th International Conference of the Biometrics Special Interest Group. IEEE, 2017. (Lecture Notes in Informatics, Bind 2017).

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

TY - GEN

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AU - Lejbølle, Aske Rasch

AU - Nasrollahi, Kamal

AU - Krogh, Benjamin

AU - Moeslund, Thomas B.

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N2 - Person re-identification is a topic which has potential to be used for applications within forensics, flow analysis and queue monitoring. It is the process of matching persons across two or more camera views, most often by extracting colour and texture based hand-crafted features, to identify similar persons. Because of challenges regarding changes in lighting between views, occlusionor even privacy issues, more focus has turned to overhead and depth based camera solutions. Therefore, we have developed a system, based on a Convolutional Neural Network (CNN) which is trained using both depth and RGB modalities to provide a fused feature. By training on a locally collected dataset, we achieve a rank-1 accuracy of 74.69%, increased by 16.00% compared to using a single modality. Furthermore, tests on two similar publicly available benchmark datasets of TVPR and DPI-T show accuracies of 77.66% and 90.36%, respectively, outperforming state-of-the-art results by 3.60% and 5.20%, respectively.

AB - Person re-identification is a topic which has potential to be used for applications within forensics, flow analysis and queue monitoring. It is the process of matching persons across two or more camera views, most often by extracting colour and texture based hand-crafted features, to identify similar persons. Because of challenges regarding changes in lighting between views, occlusionor even privacy issues, more focus has turned to overhead and depth based camera solutions. Therefore, we have developed a system, based on a Convolutional Neural Network (CNN) which is trained using both depth and RGB modalities to provide a fused feature. By training on a locally collected dataset, we achieve a rank-1 accuracy of 74.69%, increased by 16.00% compared to using a single modality. Furthermore, tests on two similar publicly available benchmark datasets of TVPR and DPI-T show accuracies of 77.66% and 90.36%, respectively, outperforming state-of-the-art results by 3.60% and 5.20%, respectively.

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Lejbølle AR, Nasrollahi K, Krogh B, Moeslund TB. Multimodal Neural Network for Overhead Person Re-identification. I 16th International Conference of the Biometrics Special Interest Group. IEEE. 2017. (Lecture Notes in Informatics, Bind 2017). https://doi.org/10.23919/BIOSIG.2017.8053514