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

Syed Farooq Ali,Ahmed Sohail Aslam,Robertas Damaševičius,Mazhar Javed Awan,Awais Yasin

doi:10.3390/app112411600

Abstract

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

Highlights

Numerous research works attempted to detect the distraction of a driver while driving, but most of them failed to achieve all the objectives of accuracy, simplicity, cost-effectiveness, and timeliness
This paper proposes a feature-based approach that outperforms state-of-the-art methods, including EM-Stat, TA, AM-Mouth, Ali, Lee, and Frid on the DrivFace, Boston University, FT-UMT, and Pointing’04 datasets
The proposed approach is compared with its variants and gives better results

Summary

Introduction

Drowsiness and distraction are the two most significant reasons for fatal car accidents in the last two decades [1]. Highway Traffic Safety Administration (NHTSA) reported that 795 causalities in vehicle crashes were the result of driver drowsiness, which was 2.3–2.5% of the total fatal crashes in the US. According to NHSTA reports, 2841 lives were claimed in the accidents due to distracted drivers in 2018, which was 6–9% of the total fatal crashes in the US [2]. At the time of a crash, 13% of distracted drivers were using their cell phones, indicating that cell phone usage was a major cause of these crashes [2]. Some signs that indicate the drowsiness of a driver include the inability to keep eyes open, frequent yawning, leaning the head forward, and face complexion change [3]

Methods

Results

Conclusion