Intelligent Real-Time Deep System for Robust Objects Tracking in Low-Light Driving Scenario

The detection of moving objects, animals, or pedestrians, as well as static objects such as road signs, is one of the fundamental tasks for assisted or self-driving vehicles. This accomplishment becomes even more difficult in low light conditions such as driving at night or inside road tunnels. Sinc...

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Autor principal:	Francesco Rundo
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	assisted driving intelligent driving scenario understanding deep learning low-light self-driving low-light driving saliency detection Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/f3975a7cedf947cb83978eaa7c07eeda
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id	oai:doaj.org-article:f3975a7cedf947cb83978eaa7c07eeda
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spelling	oai:doaj.org-article:f3975a7cedf947cb83978eaa7c07eeda2021-11-25T17:17:14ZIntelligent Real-Time Deep System for Robust Objects Tracking in Low-Light Driving Scenario10.3390/computation91101172079-3197https://doaj.org/article/f3975a7cedf947cb83978eaa7c07eeda2021-11-01T00:00:00Zhttps://www.mdpi.com/2079-3197/9/11/117https://doaj.org/toc/2079-3197The detection of moving objects, animals, or pedestrians, as well as static objects such as road signs, is one of the fundamental tasks for assisted or self-driving vehicles. This accomplishment becomes even more difficult in low light conditions such as driving at night or inside road tunnels. Since the objects found in the driving scene represent a significant collision risk, the aim of this scientific contribution is to propose an innovative pipeline that allows real time low-light driving salient objects tracking. Using a combination of the time-transient non-linear cellular networks and deep architectures with self-attention, the proposed solution will be able to perform a real-time enhancement of the low-light driving scenario frames. The downstream deep network will learn from the frames thus improved in terms of brightness in order to identify and segment salient objects by bounding-box based approach. The proposed algorithm is ongoing to be ported over a hybrid architecture consisting of a an embedded system with SPC5x Chorus MCU integrated with an automotive-grade system based on STA1295 MCU core. The performances (accuracy of about 90% and correlation coefficient of about 0.49) obtained in the experimental validation phase confirmed the effectiveness of the proposed method.Francesco RundoMDPI AGarticleassisted drivingintelligent driving scenario understandingdeep learninglow-light self-drivinglow-light driving saliency detectionElectronic computers. Computer scienceQA75.5-76.95ENComputation, Vol 9, Iss 117, p 117 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	assisted driving intelligent driving scenario understanding deep learning low-light self-driving low-light driving saliency detection Electronic computers. Computer science QA75.5-76.95
spellingShingle	assisted driving intelligent driving scenario understanding deep learning low-light self-driving low-light driving saliency detection Electronic computers. Computer science QA75.5-76.95 Francesco Rundo Intelligent Real-Time Deep System for Robust Objects Tracking in Low-Light Driving Scenario
description	The detection of moving objects, animals, or pedestrians, as well as static objects such as road signs, is one of the fundamental tasks for assisted or self-driving vehicles. This accomplishment becomes even more difficult in low light conditions such as driving at night or inside road tunnels. Since the objects found in the driving scene represent a significant collision risk, the aim of this scientific contribution is to propose an innovative pipeline that allows real time low-light driving salient objects tracking. Using a combination of the time-transient non-linear cellular networks and deep architectures with self-attention, the proposed solution will be able to perform a real-time enhancement of the low-light driving scenario frames. The downstream deep network will learn from the frames thus improved in terms of brightness in order to identify and segment salient objects by bounding-box based approach. The proposed algorithm is ongoing to be ported over a hybrid architecture consisting of a an embedded system with SPC5x Chorus MCU integrated with an automotive-grade system based on STA1295 MCU core. The performances (accuracy of about 90% and correlation coefficient of about 0.49) obtained in the experimental validation phase confirmed the effectiveness of the proposed method.
format	article
author	Francesco Rundo
author_facet	Francesco Rundo
author_sort	Francesco Rundo
title	Intelligent Real-Time Deep System for Robust Objects Tracking in Low-Light Driving Scenario
title_short	Intelligent Real-Time Deep System for Robust Objects Tracking in Low-Light Driving Scenario
title_full	Intelligent Real-Time Deep System for Robust Objects Tracking in Low-Light Driving Scenario
title_fullStr	Intelligent Real-Time Deep System for Robust Objects Tracking in Low-Light Driving Scenario
title_full_unstemmed	Intelligent Real-Time Deep System for Robust Objects Tracking in Low-Light Driving Scenario
title_sort	intelligent real-time deep system for robust objects tracking in low-light driving scenario
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/f3975a7cedf947cb83978eaa7c07eeda
work_keys_str_mv	AT francescorundo intelligentrealtimedeepsystemforrobustobjectstrackinginlowlightdrivingscenario
_version_	1718412567946199040

Intelligent Real-Time Deep System for Robust Objects Tracking in Low-Light Driving Scenario

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