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

2021

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 to 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.

Paper Citation

in Harvard Style

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, SciTePress, pages 436-443. DOI: 10.5220/0010236204360443

in Bibtex Style

@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},
}

in EndNote Style

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
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