Efficient acceleration of deep learning inference on resource-constrained edge devices: A review
Successful integration of deep neural networks (DNNs) or deep learning (DL) has resulted
in breakthroughs in many areas. However, deploying these highly accurate models for data …
in breakthroughs in many areas. However, deploying these highly accurate models for data …
Edge intelligence: Architectures, challenges, and applications
Edge intelligence refers to a set of connected systems and devices for data collection,
caching, processing, and analysis in locations close to where data is captured based on …
caching, processing, and analysis in locations close to where data is captured based on …
Edge intelligence: Empowering intelligence to the edge of network
Edge intelligence refers to a set of connected systems and devices for data collection,
caching, processing, and analysis proximity to where data are captured based on artificial …
caching, processing, and analysis proximity to where data are captured based on artificial …
Driver behavior detection and classification using deep convolutional neural networks
Driver behavior monitoring system as Intelligent Transportation Systems (ITS) have been
widely exploited to reduce the traffic accidents risk. Most previous methods for monitoring …
widely exploited to reduce the traffic accidents risk. Most previous methods for monitoring …
A survey on driver behavior analysis from in-vehicle cameras
Distracted or drowsy driving is unsafe driving behavior responsible for thousands of crashes
every year. Studying driver behavior has challenges associated with observing drivers in …
every year. Studying driver behavior has challenges associated with observing drivers in …
Distracted driver detection by combining in-vehicle and image data using deep learning
F Omerustaoglu, CO Sakar, G Kar - Applied Soft Computing, 2020 - Elsevier
Distracted driving is among the most important reasons for traffic accidents today. Recently,
there is an increasing interest in building driver assistance systems that detect the actions of …
there is an increasing interest in building driver assistance systems that detect the actions of …
Detecting and recognizing driver distraction through various data modality using machine learning: A review, recent advances, simplified framework and open …
Driver distraction is one of the main causes of fatal traffic accidents. Therefore, the ability to
detect driver inattention is essential in building a safe yet intelligent transportation system …
detect driver inattention is essential in building a safe yet intelligent transportation system …
A review of driver fatigue detection and its advances on the use of RGB-D camera and deep learning
Driver fatigue is an essential reason for traffic accidents, which poses a severe threat to
people's lives and property. In this review, we summarize the latest research findings and …
people's lives and property. In this review, we summarize the latest research findings and …
A survey on vision-based driver distraction analysis
Motor vehicle crashes are great threats to our life, which may result in numerous fatalities, as
well as tremendous economic and societal costs. Driver inattention, either distraction or …
well as tremendous economic and societal costs. Driver inattention, either distraction or …
An efficient deep learning framework for distracted driver detection
F Sajid, AR Javed, A Basharat, N Kryvinska… - IEEE …, 2021 - ieeexplore.ieee.org
The number of road accidents has constantly been increasing recently around the world. As
per the national highway traffic safety administration's investigation, 45% of vehicle crashes …
per the national highway traffic safety administration's investigation, 45% of vehicle crashes …