Advanced Assisted Car Driving in Low-light Scenarios

Francesco Rundo, Roberto Leotta, Angelo Messina, Angelo Messina, Sebastiano Battiato

2022

Abstract

The robust identification, tracking and monitoring of driving-scenario moving objects represents an extremely critical task in the safe driving target of the latest generation cars. This accomplishment becomes even more difficult in a poor light driving scenarios such as driving at night or in rough weather conditions. Since the driving detected objects could represent a significant collision risk, the aim of the proposed pipeline is to address the issue of real time low-light driving salient objects detection and tracking. By using a combined time-transient non-linear deep architecture with convolutional network embedding self attention mechanism, the authors will be able to perform a real-time assessment of the low-light driving scenario frames. The downstream deep backbone learns such features from the driving frames thus improved in terms of light exposure in order to identify and segment salient objects. The implemented algorithm is ongoing to be ported over an hybrid architectures consisting of a an embedded system with SPC5x Chorus device with an automotive-grade system based on STA1295 MCU core. The collected experimental results confirmed the effectiveness of the proposed approach.

Paper Citation

in Harvard Style

Rundo F., Leotta R., Messina A. and Battiato S. (2022). Advanced Assisted Car Driving in Low-light Scenarios. In Proceedings of the 2nd International Conference on Image Processing and Vision Engineering - Volume 1: IMPROVE, ISBN 978-989-758-563-0, pages 109-117. DOI: 10.5220/0010973300003209

in Bibtex Style

@conference{improve22,
author={Francesco Rundo and Roberto Leotta and Angelo Messina and Sebastiano Battiato},
title={Advanced Assisted Car Driving in Low-light Scenarios},
booktitle={Proceedings of the 2nd International Conference on Image Processing and Vision Engineering - Volume 1: IMPROVE,},
year={2022},
pages={109-117},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010973300003209},
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 - Advanced Assisted Car Driving in Low-light Scenarios
SN - 978-989-758-563-0
AU - Rundo F.
AU - Leotta R.
AU - Messina A.
AU - Battiato S.
PY - 2022
SP - 109
EP - 117
DO - 10.5220/0010973300003209