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Authors: Nada Hammami ; Ala Mhalla and Alexis Landrault

Affiliation: Institut Pascal, Clermont Auvergne University, France

ISBN: 978-989-758-354-4

Keyword(s): Domain Adaptation, Deep Learning, Pedestrian Detection, Tracking, Optimization, Embedded System.

Abstract: Nowadays, the analysis and the understanding of traffic scenes become a topic of great interest in several computer vision applications. Despite the presence of robust detection methods for multi-categories of objects, the performance of detectors will decrease when applied on a specific scene due to a number of constraints such as the different categories of objects, the recording time of the scene (rush hour, ordinary time), the type of traffic (simple, dense) and the type of transport infrastructure. In order to deal with this problematic, the main idea of the proposed work is to develop a domain adaptation technique to automatically adapt detectors based on deep convolutional neural network toward a specific scene and to calibrate the network parameters in order to deploy it on an embedded platform. Results are presented for the proposed adapted detector in term of global performance in mAP and execution time onto a NVIDIA Jetson TX2 board.

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Paper citation in several formats:
Hammami, N.; Mhalla, A. and Landrault, A. (2019). Domain Adaptation for Pedestrian DCNN Detector toward a Specific Scene and an Embedded Platform.In Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, ISBN 978-989-758-354-4, pages 321-327. DOI: 10.5220/0007360003210327

@conference{visapp19,
author={Nada Hammami. and Ala Mhalla. and Alexis Landrault.},
title={Domain Adaptation for Pedestrian DCNN Detector toward a Specific Scene and an Embedded Platform},
booktitle={Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP,},
year={2019},
pages={321-327},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007360003210327},
isbn={978-989-758-354-4},
}

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP,
TI - Domain Adaptation for Pedestrian DCNN Detector toward a Specific Scene and an Embedded Platform
SN - 978-989-758-354-4
AU - Hammami, N.
AU - Mhalla, A.
AU - Landrault, A.
PY - 2019
SP - 321
EP - 327
DO - 10.5220/0007360003210327

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