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

Ala Mhalla, Thierry Chateau, Sami Gazzah, Najoua Essoukri Ben Amara

2017

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.

Paper Citation

in Harvard Style

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, pages 17-23. DOI: 10.5220/0006097900170023

in Bibtex Style

@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},
}

in EndNote Style

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
AU - Mhalla A.
AU - Chateau T.
AU - Gazzah S.
AU - Essoukri Ben Amara N.
PY - 2017
SP - 17
EP - 23
DO - 10.5220/0006097900170023