Robust Seatbelt Detection and Usage Recognition for Driver Monitoring Systems
Wearing a seatbelt appropriately while driving can reduce serious crash-related injuries or deaths by about half. However, current seatbelt reminder system has multiple shortcomings, such as can be easily fooled by a "Seatbelt Warning Stopper", and cannot recognize incorrect usages for example seating in front of a buckled seatbelt or wearing a seatbelt under the arm. General seatbelt usage recognition has many challenges, to name a few, lacking of color information in Infrared (IR) cameras, strong distortion caused by wide Field of View (FoV) fisheye lens, low contrast between belt and its background, occlusions caused by hands or hair, and imaging blurry. In this paper, we introduce a novel general seatbelt detection and usage recognition framework to resolve the above challenges. Our method consists of three components: a local predictor, a global assembler, and a shape modeling process. Our approach can be applied to the driver in the Driver Monitoring System (DMS) or general passengers in the Occupant Monitoring System (OMS) for various camera modalities. Experiment results on both DMS and OMS are provided to demonstrate the accuracy and robustness of the proposed approach.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Neuromorphic Seatbelt State Detection for In-Cabin Monitoring with Event Cameras
Neuromorphic vision sensors, or event cameras, differ from conventional cameras in that they do not capture images at a specified rate. Instead, they asynchronously log local brightness changes at each pixel. As a result…
Binary ClassificationDMD: A Large-Scale Multi-Modal Driver Monitoring Dataset for Attention and Alertness Analysis
Vision is the richest and most cost-effective technology for Driver Monitoring Systems (DMS), especially after the recent success of Deep Learning (DL) methods. The lack of sufficiently large and comprehensive datasets i…
CPUInCarEmo: A Multimodal Dataset for In-Cabin Emotion Recognition and Driver State Monitoring
Understanding driver emotion and state is critical for the next generation of intelligent in-cabin systems that ensure safety and enhance human-vehicle interaction. However, existing public datasets for in-cabin affectiv…
Multimodal Emotion RecognitionA Survey and Tutorial of EEG-Based Brain Monitoring for Driver State Analysis
Drivers cognitive and physiological states affect their ability to control their vehicles. Thus, these driver states are important to the safety of automobiles. The design of advanced driver assistance systems (ADAS) or …
Autonomous VehiclesEEGElectroencephalogram (EEG)Blinking Beyond EAR: A Stable Eyelid Angle Metric for Driver Drowsiness Detection and Data Augmentation
Detecting driver drowsiness reliably is crucial for enhancing road safety and supporting advanced driver assistance systems (ADAS). We introduce the Eyelid Angle (ELA), a novel, reproducible metric of eye openness derive…
Data Augmentation