The invention discloses machine learning-based seatbelt detection and use identification using fiducial markers, and specifically discloses a system and method for machine learning-based seatbelt position detection and classification. A number of fiducial markers are placed on a vehicle seatbelt. A camera or other sensor is placed within a vehicle to capture an image or other data relating to the location of the fiducial marker when using the seatbelt. One or more models, such as a machine learning model, may then determine a spatial position of the fiducial marker from the captured image information, and determine a wear state of the seatbelt. In particular, the system may determine whether the seatbelt is worn in one or more improper conditions, such as not worn or in an unsafe or dangerous manner, and if so, the system may alert the vehicle to take corrective actions. In this manner, the system provides for continuous and real-time monitoring of the seatbelt to improve the use and safety of the seatbelt.
公开了使用基准标记的基于机器学习的安全带检测和使用识别,具体公开了用于基于机器学习的安全带位置检测和分类的系统和方法。许多基准标记放置在车辆安全带上。相机或其他传感器放置在车辆内,以在使用安全带时捕获图像或与基准标记的位置相关的其他数据。诸如机器学习模型之类的一个或更多个模型然后可以从捕获的图像信息确定基准标记的空间位置,并确定安全带的佩戴状态。特别地,系统可以确定安全带是否以一种或更多种不当的状态被佩戴,例如没有佩戴或者以不安全或危险的方式佩戴,并且如果是这样,系统可以警告车辆采取纠正措施。通过这种方式,系统提供对安全带的持续和实时监控,以提高安全带的使用和安全性。
MACHINE LEARNING-BASED SEATBELT DETECTION AND USAGE RECOGNITION USING FIDUCIAL MARKING
使用基准标记的基于机器学习的安全带检测和使用识别
2021-12-21
Patent
Electronic Resource
Chinese
MACHINE LEARNING-BASED SEATBELT DETECTION AND USAGE RECOGNITION USING FIDUCIAL MARKING
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