Objects Motion Detection in Domain-adapted Assisted Driving
Francesco Rundo, Roberto Leotta, Sebastiano Battiato
2022
Abstract
The modern Advanced Driver Assistance Systems (ADAS) contributed to reduce road accidents due to the driver’s inexperience or unexpected scenarios. ADAS technologies allow the intelligent monitoring of the driving scenario. Recently, estimation of the visual saliency i.e. the part of the visual scene in which the driver put high visual attention has received significant research interests. This work makes further contributions to video saliency investigation for automotive applications. The difficulty to collect robust labeled data as well as the several features of the driving scenarios require the usage of such domain adaptation methods. A new approach to Gradient-Reversal domain adaptation in deep architectures is proposed. More in detail, the proposed pipeline enables an intelligent identification and segmentation of the motion salient objects in different driving scenarios and domains. The performed test results confirmed the effectiveness of the overall proposed pipeline.
DownloadPaper Citation
in Harvard Style
Rundo F., Leotta R. and Battiato S. (2022). Objects Motion Detection in Domain-adapted Assisted Driving. In Proceedings of the 2nd International Conference on Image Processing and Vision Engineering - Volume 1: IMPROVE, ISBN 978-989-758-563-0, pages 101-108. DOI: 10.5220/0010973100003209
in Bibtex Style
@conference{improve22,
author={Francesco Rundo and Roberto Leotta and Sebastiano Battiato},
title={Objects Motion Detection in Domain-adapted Assisted Driving},
booktitle={Proceedings of the 2nd International Conference on Image Processing and Vision Engineering - Volume 1: IMPROVE,},
year={2022},
pages={101-108},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010973100003209},
isbn={978-989-758-563-0},
}
in EndNote Style
TY - CONF
JO - Proceedings of the 2nd International Conference on Image Processing and Vision Engineering - Volume 1: IMPROVE,
TI - Objects Motion Detection in Domain-adapted Assisted Driving
SN - 978-989-758-563-0
AU - Rundo F.
AU - Leotta R.
AU - Battiato S.
PY - 2022
SP - 101
EP - 108
DO - 10.5220/0010973100003209