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Open AccessArticle

Track-Before-Detect Framework-Based Vehicle Monocular Vision Sensors

Laboratory SATIE (Systèmes et Applications des Technologies de l’Information et de l’Energie), CNRS (UMR 8029), Université Paris Sud, 91405 Orsay, France
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Sensors 2019, 19(3), 560; https://doi.org/10.3390/s19030560
Received: 14 December 2018 / Revised: 19 January 2019 / Accepted: 25 January 2019 / Published: 29 January 2019
(This article belongs to the Special Issue Sensors Applications in Intelligent Vehicle)
This paper proposes a Track-before-Detect framework for a multibody motion segmentation (named TbD-SfM). Our contribution relies on a tightly coupled tracking before detection strategy intended to reduce the complexity of existing Multibody Structure from Motion approaches. Efforts were done towards an algorithm variant closer and aimed to a further embedded implementation for dynamic scene analysis while enhancing processing time performances. This generic motion segmentation approach can be transposed to several transportation sensor systems since no constraints are considered on segmented motions (6-DOF model). The tracking scheme is analyzed and its performance is evaluated under thorough experimental conditions including full-scale driving scenarios from known and available datasets. Results on challenging scenarios including the presence of multiple and simultaneous moving objects observed from a moving camera are reported and discussed. View Full-Text
Keywords: motion segmentation; monocular camera; structure from motion; embedded systems motion segmentation; monocular camera; structure from motion; embedded systems
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MDPI and ACS Style

Gonzalez, H.; Rodriguez, S.; Elouardi, A. Track-Before-Detect Framework-Based Vehicle Monocular Vision Sensors. Sensors 2019, 19, 560.

AMA Style

Gonzalez H, Rodriguez S, Elouardi A. Track-Before-Detect Framework-Based Vehicle Monocular Vision Sensors. Sensors. 2019; 19(3):560.

Chicago/Turabian Style

Gonzalez, Hernan; Rodriguez, Sergio; Elouardi, Abdelhafid. 2019. "Track-Before-Detect Framework-Based Vehicle Monocular Vision Sensors" Sensors 19, no. 3: 560.

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