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Specialization of a Generic Pedestrian Detector to a Specific Traffic Scene by the Sequential Monte-Carlo Filter and the Faster R-CNN

Topics: Camera Networks and Vision; Image Formation, Acquisition Devices and Sensors; Object Detection and Localization; Video Surveillance and Event Detection; Visual Attention and Image Saliency

Authors: Ala Mhalla 1 ; Thierry Chateau 2 ; Sami Gazzah 3 and Najoua Essoukri Ben Amara 3

Affiliations: 1 LATIS ENISo, University of Sousse, Institut Pascal and Blaise Pascal University, Tunisia ; 2 Institut Pascal and Blaise Pascal University, France ; 3 LATIS ENISo and University of Sousse, Tunisia

ISBN: 978-989-758-225-7

ISSN: 2184-4321

Keyword(s): Transfer Learning, Deep Learning, Faster R-CNN, Sequential Monte Carlo Filter (SMC), Pedestrian Detection.

Related Ontology Subjects/Areas/Topics: Applications and Services ; Camera Networks and Vision ; Computer Vision, Visualization and Computer Graphics ; Image and Video Analysis ; Image Formation and Preprocessing ; Image Formation, Acquisition Devices and Sensors ; Motion, Tracking and Stereo Vision ; Video Surveillance and Event Detection ; Visual Attention and Image Saliency

Abstract: The performance of a generic pedestrian detector decreases significantly when it is applied to a specific scene due to the large variation between the source dataset used to train the generic detector and samples in the target scene. In this paper, we suggest a new approach to automatically specialize a scene-specific pedestrian detector starting with a generic detector in video surveillance without further manually labeling any samples under a novel transfer learning framework. The main idea is to consider a deep detector as a function that generates realizations from the probability distribution of the pedestrian to be detected in the target. Our contribution is to approximate this target probability distribution with a set of samples and an associated specialized deep detector estimated in a sequential Monte Carlo filter framework. The effectiveness of the proposed framework is demonstrated through experiments on two public surveillance datasets. Compared with a generic pedestrian detector and the state-of-the-art methods, our proposed framework presents encouraging results. (More)

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Paper citation in several formats:
Mhalla, A.; Chateau, T.; Gazzah, S. and Essoukri Ben Amara, N. (2017). Specialization of a Generic Pedestrian Detector to a Specific Traffic Scene by the Sequential Monte-Carlo Filter and the Faster R-CNN. In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2017) ISBN 978-989-758-225-7; ISSN 2184-4321, pages 17-23. DOI: 10.5220/0006097900170023

@conference{visapp17,
author={Ala Mhalla. and Thierry Chateau. and Sami Gazzah. and Najoua {Essoukri Ben Amara}.},
title={Specialization of a Generic Pedestrian Detector to a Specific Traffic Scene by the Sequential Monte-Carlo Filter and the Faster R-CNN},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2017)},
year={2017},
pages={17-23},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006097900170023},
isbn={978-989-758-225-7},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2017)
TI - Specialization of a Generic Pedestrian Detector to a Specific Traffic Scene by the Sequential Monte-Carlo Filter and the Faster R-CNN
SN - 978-989-758-225-7
IS - 2184-4321
AU - Mhalla, A.
AU - Chateau, T.
AU - Gazzah, S.
AU - Essoukri Ben Amara, N.
PY - 2017
SP - 17
EP - 23
DO - 10.5220/0006097900170023

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