Papers Papers/2022 Papers Papers/2022



Authors: Francesco Rundo 1 ; Roberto Leotta 2 and Sebastiano Battiato 2

Affiliations: 1 STMicroelectronics, ADG Central R&D, Catania, Italy ; 2 Department of Mathematics and Computer Science, University of Catania, Catania, Italy

Keyword(s): ADAS, Automotive, Deep Learning, Road Classification, Intelligent Suspension.

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.


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Paper citation in several formats:
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 - IMPROVE; ISBN 978-989-758-563-0; ISSN 2795-4943, SciTePress, pages 101-108. DOI: 10.5220/0010973100003209

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 - IMPROVE},


JO - Proceedings of the 2nd International Conference on Image Processing and Vision Engineering - IMPROVE
TI - Objects Motion Detection in Domain-adapted Assisted Driving
SN - 978-989-758-563-0
IS - 2795-4943
AU - Rundo, F.
AU - Leotta, R.
AU - Battiato, S.
PY - 2022
SP - 101
EP - 108
DO - 10.5220/0010973100003209
PB - SciTePress