Analysis and research on the images of drivers and passengers wearing seat belt in traffic inspection

Abstract

It is common to see drivers and passengers not wearing seat belt in China, which is a traffic violation, thereby to strengthen supervision by resorting to advanced technical means is imminent. In the existing research, the detection effect is favorable when there is visible difference between the seat belt and the background image. However, in the opposing situation, it can easily lead to missed detection or mis-detection, and greatly reducing the detection accuracy and making it difficult to meet the demands of intelligent and automatic traffic monitoring. Study on the images of drivers and passengers wearing seat belt in traffic inspection covers three aspects: vehicle target positioning, driver and passenger area positioning and seat belt wearing behavior decision. Vehicle target positioning studies the enhanced vehicle features and looks for a vehicle classification model that is suitable for enhancing the features, and obtains the determined vehicle area from the study detection. Driver and passenger area positioning studies a multi-level joint supervised learning method according to the inclusive relationship between the non-rigid human body and the rigid vehicle body, and gets the optimal driver and passenger area by designing the multi-level learning classifier. Seat belt wearing behavior decision studies from the method of linear target recognition and uses the perceptual grouping mechanism of human visual system for robust seat belt detection. In the experimental phase, we validated the validity and accuracy, and the experiment images were from the practical traffic junctions. The results show that the method proposed in this paper can carry out Seat Belt detection accurately and effectively. Compared with other Seat Belt target detection methods, it has high detection rate and low false positive rate, which can achieve the accurate detection of Seat Belt in intelligent transportation.