YOLO-Based Deep Learning Design for In-Cabin Monitoring System with Fisheye-Lens Camera

Poon, Yen-Sok; Liu, Yu-Hsuan; Fan, Chih‐Peng

doi:10.1109/icce53296.2022.9730235

Cited by 19 publications

(7 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Multiple works have been reported to improve the safety, security and comfort inside the vehicle's cabin and various state-of-the-art technologies are being proposed and developed for this challenge. Poon et al [1] performed deep learning models, including versions of YOLO, to monitor the driving behavior and in-vehicle occupants. Lee et al [12] analyzes the performance of many existing deep learning models and proposes a new occupancy detection method.…”

Section: Vision Methods For In Cabin Occupant Monitoringmentioning

confidence: 99%

“…An airbag is one of the passive safety systems available in a car. It saves the lives of the driver and passengers but can also cause serious injury or even death of children who are in a rear-facing child seat in the front passenger seat [1]. These injuries can be limited via a controlled deployment of airbags based on passenger's classification into child or adult.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

In-cabin occupant monitoring system based on improved Yolo, deep reinforcement learning, and multi-task CNN for autonomous driving

KHRAIEF OULED AZAIZ,

DACLEU NDENGUE

2023

Fifteenth International Conference on Machine Vision (ICMV 2022)

View full text Add to dashboard Cite

Vehicle in-cabin occupant monitoring system is becoming a crucial feature of the automobile industry and challenging research topic to enhance both vehicle safety, security, and comfort of conventional and future intelligent vehicles. Precise information about the number, position, and characteristics of occupants as well as objects located inside the vehicle must be available. Current industrial systems for seat occupancy detection are based on multiple weight sensors, capacitive sensors, electric field, or ultrasonic sensors. They cannot necessarily make the right distinction in borderline cases. A simple pressure sensor cannot tell whether the weight on the seat comes from a person or an inanimate object. Recently, the Artificial Intelligence (AI) based advanced systems have attracted attention for various fields such as automobile industry. Especially, with the advancement of deep learning that has shown very high classification accuracies compared to hand-crafted features on various computer vision tasks. For the above reasons, we propose a new automatic AI occupant monitoring system based on two cameras installed inside the vehicle. Our goal is to develop an automatic detection and recognition system with high accuracy performance, low computational cost and small weight model. Our system fuses our modified deep convolutional network Yolo model and deep reinforcement learning to detect and classify passengers and objects inside the vehicle. It can predict the gender, the age and the emotion of occupants based on our proposed muti task convolutional neural networks. In our end-to-end system, this approach is more efficient time and memory wise by solving all the tasks in the same process and storing a single CNN instead of storing a CNN for each task. Principal applications of our system are intelligent airbag management, seat belt reminder, life presence and in shared cabin preferences. We perform comparative evaluation based on the public datasets SVIRO, TiCaM, Aff-Wild and Adience dataset to demonstrate the superior performance of our proposed system.

show abstract

Section: Vision Methods For In Cabin Occupant Monitoringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

In-cabin occupant monitoring system based on improved Yolo, deep reinforcement learning, and multi-task CNN for autonomous driving

KHRAIEF OULED AZAIZ,

DACLEU NDENGUE

2023

Fifteenth International Conference on Machine Vision (ICMV 2022)

View full text Add to dashboard Cite

show abstract

“…Source [2] shows a technology or research project that utilizes the YOLO (You Only Look Once) deep learning algorithm for the purpose of monitoring the interior of a vehicle's cabin. Specifically, it employs a fisheye-lens camera, which has a wide-angle view, to capture the interior environment of the vehicle.…”

Section: Review Of Litarature Sourcesmentioning

confidence: 99%

An Alternative to Vending Machines

Lozynskyi,

Yurchak

2023

ACPS

View full text Add to dashboard Cite

In this review article for a smart vending refrigerator, the contours of the future device are thought out and outlined and all its advantages are described. This device will be controlled using Computer Vision and some other features. The main control unit will be Raspberry PI, since it is the best for this device. Also, a web application was developed in which the user registers, and the applica- tion itself transmits the user's information through an API that will be developed to communicate with the web server, and the web server will store this information. This article will analyze the systems that have been already on the market and their pros and cons, as well as consider the design, implementation and functionality of a smart vend- ing refrigerator. Also, the paper will consider key requirements for this system, technologies used, and approaches to integration with existing infrastructures. This design plays an important role in providing comfort and productivity in various fields of activity and can be applied in many areas.

show abstract

“…Recently various companies have also reported their in-cabin monitoring systems for improving the safety and security of the occupants inside the vehicle [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33]. Poon et al have performed deep learning models, including versions of YOLO, to monitor the driving behavior and in-vehicle occupants [34]. Rong et al surveyed the AI methods involved in in-cabin monitoring [9].…”

Section: Related Workmentioning

confidence: 99%

In-Cabin Monitoring System for Autonomous Vehicles

Mishra

Lee

Kim

et al. 2022

Sensors

View full text Add to dashboard Cite

In this paper, we have demonstrated a robust in-cabin monitoring system (IMS) for safety, security, surveillance, and monitoring, including privacy concerns for personal and shared autonomous vehicles (AVs). It consists of a set of monitoring cameras and an onboard device (OBD) equipped with artificial intelligence (AI). Hereafter, this combination of a camera and an OBD is referred to as the AI camera. We have investigated the issues for mobility services in higher levels of autonomous driving, what needs to be monitored, how to monitor, etc. Our proposed IMS is an on-device AI system that indigenously has improved the privacy of the users. Furthermore, we have enlisted the essential actions to be considered in an IMS and developed an appropriate database (DB). Our DB consists of multifaced scenarios important for monitoring the in-cabin of the higher-level AVs. Moreover, we have compared popular AI models applied for object and occupant recognition. In addition, our DB is available on request to support the research on the development of seamless monitoring of the in-cabin higher levels of autonomous driving for the assurance of safety and security.

show abstract

YOLO-Based Deep Learning Design for In-Cabin Monitoring System with Fisheye-Lens Camera

Cited by 19 publications

References 7 publications

In-cabin occupant monitoring system based on improved Yolo, deep reinforcement learning, and multi-task CNN for autonomous driving

In-cabin occupant monitoring system based on improved Yolo, deep reinforcement learning, and multi-task CNN for autonomous driving

An Alternative to Vending Machines

In-Cabin Monitoring System for Autonomous Vehicles

Contact Info

Product

Resources

About