Deep face recognition: A survey
Deep learning applies multiple processing layers to learn representations of data with
multiple levels of feature extraction. This emerging technique has reshaped the research …
multiple levels of feature extraction. This emerging technique has reshaped the research …
Deep learning-based monocular depth estimation methods—a state-of-the-art review
Monocular depth estimation from Red-Green-Blue (RGB) images is a well-studied ill-posed
problem in computer vision which has been investigated intensively over the past decade …
problem in computer vision which has been investigated intensively over the past decade …
Predicting the driver's focus of attention: the dr (eye) ve project
In this work we aim to predict the driver's focus of attention. The goal is to estimate what a
person would pay attention to while driving, and which part of the scene around the vehicle …
person would pay attention to while driving, and which part of the scene around the vehicle …
NGDNet: Nonuniform Gaussian-label distribution learning for infrared head pose estimation and on-task behavior understanding in the classroom
Head pose estimation (HPE) under active infrared (IR) illumination has attracted much
attention in the fields of computer vision and machine learning. However, IRHPE often …
attention in the fields of computer vision and machine learning. However, IRHPE often …
Datid-3d: Diversity-preserved domain adaptation using text-to-image diffusion for 3d generative model
Recent 3D generative models have achieved remarkable performance in synthesizing high
resolution photorealistic images with view consistency and detailed 3D shapes, but training …
resolution photorealistic images with view consistency and detailed 3D shapes, but training …
Whenet: Real-time fine-grained estimation for wide range head pose
Y Zhou, J Gregson - arXiv preprint arXiv:2005.10353, 2020 - arxiv.org
We present an end-to-end head-pose estimation network designed to predict Euler angles
through the full range head yaws from a single RGB image. Existing methods perform well …
through the full range head yaws from a single RGB image. Existing methods perform well …
Dmd: 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 …
especially after the recent success of Deep Learning (DL) methods. The lack of sufficiently …
Exploiting temporal consistency for real-time video depth estimation
Accuracy of depth estimation from static images has been significantly improved recently, by
exploiting hierarchical features from deep convolutional neural networks (CNNs). Compared …
exploiting hierarchical features from deep convolutional neural networks (CNNs). Compared …
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 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 …