Pose Estimation of Driver’s Head Panning Based on Interpolation and Motion Vectors under a Boosting Framework

Over the last decade, a driver’s distraction has gained popularity due to its increased significance and high impact on road accidents. Various factors, such as mood disorder, anxiety, nervousness, illness, loud music, head rotation, contribute significantly causing distraction. Many solutions have been proposed address this problem; however, various aspects of it are still unresolved. The study proposes novel geometric spatial scale-invariant features under boosting framework for detecting panning. These calculated using facial landmark detection algorithms, including Active Shape Model (ASM) Boosted Regression with Markov Networks (BoRMaN). approach is compared six existing state-of-the-art approaches four benchmark datasets, DrivFace dataset, Boston University (BU) FT-UMT Pointing’04 dataset. outperforms achieving an accuracy 94.43%, 92.08%, 96.63%, 83.25% standard datasets.