Multi-Stage Dynamic Batching and On-Demand I-Vector Clustering for Cost-effective Video Surveillance

David Montero; Luis Unzueta; Jon Goenetxea; Nerea Aranjuelo; Estibaliz Loyo; Oihana Otaegui; Marcos Nieto

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Paper

Multi-Stage Dynamic Batching and On-Demand I-Vector Clustering for Cost-effective Video Surveillance

Topics: Camera Networks and Vision; Face and Expression Recognition; Video Surveillance and Event Detection

In Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5 VISAPP: VISAPP, 436-443, 2021

Authors: David Montero ; Luis Unzueta ; Jon Goenetxea ; Nerea Aranjuelo ; Estibaliz Loyo ; Oihana Otaegui and Marcos Nieto

Affiliation: Vicomtech, Mikeletegi 57, 20009 Donostia-SanSebastian, Spain

Keyword(s): Face Recognition, Face Clustering, Video-Surveillance.

Abstract: In this paper, we present a cost-effective Video-Surveillance System (VSS) for face recognition and online clustering of unknown individuals at large scale. We aim to obtain Performance Indicators (PIs) for people flow monitoring in large infrastructures, without storing any biometric information. For this purpose, we focus on how to take advantage of a central GPU-enabled computing server, connected to a set of video-surveillance cameras, to automatically register new identities and update their descriptive data as they are re-identified. The proposed method comprises two main procedures executed in parallel. A Multi-Stage Dynamic Batching (MSDB) procedure efficiently extracts facial identity vectors (i-vectors) from captured images. At the same time, an On-Demand I-Vector Clustering (ODIVC) procedure clusters the i-vectors into identities. This clustering algorithm is designed to progressively adapt to the increasing data scale, with a lower decrease in its effectiveness compared t o other alternatives. Experimental results show that ODIVC achieves state-of-the-art results in well-known large scale datasets and that our VSS can detect, recognize and cluster in real time faces coming from up to 40 cameras with a central off-the-shelf GPU-enabled computing server. (More)

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Paper citation in several formats:

Montero, D.; Unzueta, L.; Goenetxea, J.; Aranjuelo, N.; Loyo, E.; Otaegui, O. and Nieto, M. (2021). Multi-Stage Dynamic Batching and On-Demand I-Vector Clustering for Cost-effective Video Surveillance. In Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 5: VISAPP; ISBN 978-989-758-488-6; ISSN 2184-4321, SciTePress, pages 436-443. DOI: 10.5220/0010236204360443

@conference{visapp21,
author={David Montero. and Luis Unzueta. and Jon Goenetxea. and Nerea Aranjuelo. and Estibaliz Loyo. and Oihana Otaegui. and Marcos Nieto.},
title={Multi-Stage Dynamic Batching and On-Demand I-Vector Clustering for Cost-effective Video Surveillance},
booktitle={Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 5: VISAPP},
year={2021},
pages={436-443},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010236204360443},
isbn={978-989-758-488-6},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 5: VISAPP
TI - Multi-Stage Dynamic Batching and On-Demand I-Vector Clustering for Cost-effective Video Surveillance
SN - 978-989-758-488-6
IS - 2184-4321
AU - Montero, D.
AU - Unzueta, L.
AU - Goenetxea, J.
AU - Aranjuelo, N.
AU - Loyo, E.
AU - Otaegui, O.
AU - Nieto, M.
PY - 2021
SP - 436
EP - 443
DO - 10.5220/0010236204360443
PB - SciTePress