Benefits of a Portable Warning System Adaptable to Vehicles Dedicated for Seat Belts Detection

Zadobrischi, Eduard; Cosovanu, Lucian-Mihai; Dimian, Mihai

doi:10.1109/icstcc50638.2020.9259723

Cited by 1 publication

(1 citation statement)

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“…There are several methods to classify seat belt usage in cars, such as using 2D cameras inside the car. A portable warning system with data collected inside the car is proposed in [5]. The underlying detection method of the seat belt is a hard coded template, which utilizes the assumption, that the seat belt has an degree of around 45 degrees.…”

Section: Introductionmentioning

confidence: 99%

Automated Quantification of Occupant Posture and Shoulder Belt Fit Using Safety Specific Key Points

Hartleitner

Koppisetty

Bohman

2022

IEEE Open J. Intell. Transp. Syst.

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Virtual evaluation of automotive safety with variation in occupant posture and shoulder belt fit is gaining importance, and there is a need of methods facilitating analysis of occupant postures in driving studies. This study is aimed to develop an AI-based computer vision method to automatically quantify occupant posture and shoulder belt position over time in a car. Traceable defined key points on the occupant were related with the shoulder belt and quantified over time in real 3D coordinates by predefined key measurements, utilising the underlying spatial information of a Intel RealSense 3D Camera. The key points are defined as traceable key points relevant to relate the occupant to the vehicle environment and to estimate shoulder belt position. Key point prediction results suggest an average deviation of around 1cm per coordinate, which enable a reliable spatial categorisation of the respective tracked occupant by analysing the key measurements. This method providing continuous information of the occupant position and belt fit will be useful to identify common occupant postures as well as more extreme postures, to be used for expanding variations in postures for vehicle safety assessments.

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Section: Introductionmentioning

confidence: 99%