A Novel Real-time Driver Monitoring System Based on Deep Convolutional Neural Network

Zhao, Yiheng; Mammeri, Abdelhamid; Boukerche, Azzedine

doi:10.1109/rose.2019.8790428

Cited by 7 publications

(3 citation statements)

References 23 publications

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“…Y. Zhao et al [ 106 ] propose a novel real-time DMSs based on deep CNN to monitor drivers’ behavior and detect distractions. It uses video input from an in-car camera and employs CNNs to analyze the driver’s facial expressions and head movements to assess their attentiveness.…”

Section: Discussion—methodologymentioning

confidence: 99%

“…The databases include IEEE Xplore and MDPI. The papers [106][107][108][109][110][111][112][113][114] discuss a variety of approaches to DMSs. These include some of the key methods like (i) the powerful technique employing DL, which is used to extract features from images and videos.…”

Section: Search Terms and Recent Trends In Drivermentioning

confidence: 99%

“…For instance, a DMS could combine data from a camera, an eye tracker, and a heart rate monitor to provide a more comprehensive assessment of the driver's state. The papers [106][107][108][109][110][111][112][113][114] discuss a variety of approaches to DMSs. These include some of the key methods like (i) the powerful technique employing DL, which is used to extract features from images and videos.…”

Section: Search Terms and Recent Trends In Drivermentioning

confidence: 99%

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Object Detection, Recognition, and Tracking Algorithms for ADASs—A Study on Recent Trends

Malligere Shivanna,

Guo

2023

Sensors

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Advanced driver assistance systems (ADASs) are becoming increasingly common in modern-day vehicles, as they not only improve safety and reduce accidents but also aid in smoother and easier driving. ADASs rely on a variety of sensors such as cameras, radars, lidars, and a combination of sensors, to perceive their surroundings and identify and track objects on the road. The key components of ADASs are object detection, recognition, and tracking algorithms that allow vehicles to identify and track other objects on the road, such as other vehicles, pedestrians, cyclists, obstacles, traffic signs, traffic lights, etc. This information is then used to warn the driver of potential hazards or used by the ADAS itself to take corrective actions to avoid an accident. This paper provides a review of prominent state-of-the-art object detection, recognition, and tracking algorithms used in different functionalities of ADASs. The paper begins by introducing the history and fundamentals of ADASs followed by reviewing recent trends in various ADAS algorithms and their functionalities, along with the datasets employed. The paper concludes by discussing the future of object detection, recognition, and tracking algorithms for ADASs. The paper also discusses the need for more research on object detection, recognition, and tracking in challenging environments, such as those with low visibility or high traffic density.

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Section: Discussion—methodologymentioning

confidence: 99%