Title: A new method of vision-based seat belt detection

Authors: Zhongming Yang; Hui Xiong; Zhaoquan Cai; Yu Peng

Addresses: College of Computer Engineering Technical, Guangdong Polytechnic of Science and Technology, Zhuhai, Guangdong, China ' School of Software Engineering, South China University of Technology, Guangzhou, Guangdong, China ' Science and Technology Department, Huizhou University, Huizhou, Guangdong, China ' School of Software Engineering, South China University of Technology, Guangzhou, Guangdong, China

Abstract: In the traffic management system, it can greatly improve the management efficiency if the algorithm that monitors automatically detects whether the driver fastens the seat belt; however, currently prevalent detecting methods cannot achieve satisfactory results in aspects of the detecting rate, the image quality requirement and the colour difference between seat belt and the surrounding environment. We propose a method of seat belt detection based on visual positioning. The algorithm locates the window according to the licence plate position and the contour statistics obtained from the gradient. The face detection is used to adjust and determine the seat belt detection area in the window. Finally, the method of seat belt detection based on the connected area is used to detect whether the seat belt is fastened. Experiments show that the successful rate of the proposed method is much higher than other existing methods, and satisfactory results are obtained.

Keywords: seat belt detection; connected components; big data in traffic; structured image data.

DOI: 10.1504/IJES.2019.103992

International Journal of Embedded Systems, 2019 Vol.11 No.6, pp.755 - 763

Received: 07 May 2017
Accepted: 18 Nov 2017

Published online: 02 Dec 2019 *

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