An Intelligent Mobile App to Detect Drowsy Driving with Artificial Intelligence

2021 ◽  
Author(s):  
Thomas Xiao ◽  
Yu Sun
Keyword(s):  
Artificial Intelligence ◽  
Computer Vision ◽  
Normal Control ◽  
Qualitative Evaluation ◽  
Mobile App ◽  
Drowsy Driving ◽  
Evaluation Approach ◽  
Control Situation ◽  
Driving Condition

Drowsy driving is lethal- 793 died from accidents related to drowsy driving and 91000 accidents related to drowsy driving occurred [1]. However, drowsy driving and accidents related to drowsy driving are preventable. In this paper, we address the problem through an application that uses artificial intelligence to detect the eye openness of the user. The application can detect the eyes of the user via computer vision. Based on the user’s eye openness and frequencies, the sleepy driving condition can be inferred by this application. We applied our application to actual driving environments on the highway, both day and night, as well as within a normal control situation using a qualitative evaluation approach. The result shows that it is 88% effective during the day and 75% effective during nighttime. This result reveals effectiveness and accuracy of detection during daytime application under controlled testing, which is more flexible and efficient comparing to previous works. Effectiveness and accuracy for nighttime detection and detections with the presence of other distractions can be further improved.

МЕТОДЫ ДОСТИЖЕНИЯ МАКСИМАЛЬНОЙ ЭФФЕКТИВНОСТИ ПЛАТФОРМЫ ПРОТОТИПИРОВАНИЯ ВЫСОКОПРОИЗВОДИТЕЛЬНЫХ СИСТЕМ НА КРИСТАЛЛЕ НА ЗАДАЧАХ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА

2020 ◽  
Vol 96 (3s) ◽  
pp. 585-588
Author(s):  
С.Е. Фролова ◽  
Е.С. Янакова
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Computer Vision ◽  
High Performance ◽  
Systems On Chip ◽  
High Performance Systems ◽  
On Chip ◽  
Network Technologies ◽  
Neural Network Technologies

Предлагаются методы построения платформ прототипирования высокопроизводительных систем на кристалле для задач искусственного интеллекта. Изложены требования к платформам подобного класса и принципы изменения проекта СнК для имплементации в прототип. Рассматриваются методы отладки проектов на платформе прототипирования. Приведены результаты работ алгоритмов компьютерного зрения с использованием нейросетевых технологий на FPGA-прототипе семантических ядер ELcore. Methods have been proposed for building prototyping platforms for high-performance systems-on-chip for artificial intelligence tasks. The requirements for platforms of this class and the principles for changing the design of the SoC for implementation in the prototype have been described as well as methods of debugging projects on the prototyping platform. The results of the work of computer vision algorithms using neural network technologies on the FPGA prototype of the ELcore semantic cores have been presented.

scholarly journals Deep learning-enabled medical computer vision

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Andre Esteva ◽  
Katherine Chou ◽  
Serena Yeung ◽  
Nikhil Naik ◽  
Ali Madani ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Computer Vision ◽  
Deep Learning ◽  
Medical Imaging ◽  
Real World ◽  
Recent Progress ◽  
Medical Applications ◽  
Modern Computer ◽  
Medical Computer

AbstractA decade of unprecedented progress in artificial intelligence (AI) has demonstrated the potential for many fields—including medicine—to benefit from the insights that AI techniques can extract from data. Here we survey recent progress in the development of modern computer vision techniques—powered by deep learning—for medical applications, focusing on medical imaging, medical video, and clinical deployment. We start by briefly summarizing a decade of progress in convolutional neural networks, including the vision tasks they enable, in the context of healthcare. Next, we discuss several example medical imaging applications that stand to benefit—including cardiology, pathology, dermatology, ophthalmology–and propose new avenues for continued work. We then expand into general medical video, highlighting ways in which clinical workflows can integrate computer vision to enhance care. Finally, we discuss the challenges and hurdles required for real-world clinical deployment of these technologies.

scholarly journals From Data Journalism to Artificial Intelligence: Challenges Faced by La Nación in Implementing Computer Vision in News Reporting

2021 ◽  
Vol 24 (3) ◽  
pp. 1-40
Author(s):  
Mathias-Felipe de-Lima-Santos ◽  
Ramón Salaverría
Keyword(s):  
Artificial Intelligence ◽  
Computer Vision ◽  
Latin American ◽  
Implementation Process ◽  
Media Industry ◽  
Technological Infrastructure ◽  
News Reporting ◽  
Technological Advances ◽  
High Resolution Imagery ◽  
Data Journalism

Journalism is at a radical point of change that requires organizations to come up with new ideas and formats for news reporting. Additionally, the notable surge of data, sensors and technological advances in the mobile segment has brought immeasurable benefits to many fields of journalistic practice (data journalism in particular). Given the relative novelty and complexity of implementing artificial intelligence (AI) in journalism, few areas have managed to deploy tailored AI solutions in the media industry. In this study, through a mixed-method approach that combines both participant observations and interviews, we explain the hurdles and obstacles to deploying computer vision news projects, a subset of AI, in a leading Latin American news organization, the Argentine newspaper La Nación. Our results highlight four broad difficulties in implementing computer vision projects that involve satellite imagery: a lack of high-resolution imagery, the unavailability of technological infrastructure, the absence of qualified personnel to develop such codes, and a lengthy and costly implementation process that requires significant investment. This article concludes with a discussion of the centrality of AI solutions in the hands of big tech corporations.

scholarly journals EVALUASI IMPLEMENTASI KEBIJAKAN NO. 12 TAHUN 2012 TENTANG TUNTUTAN PENGELOLAAN KURIKULUM

2017 ◽  
Vol 8 (1) ◽  
pp. 1
Author(s):  
Dwi Deswary
Keyword(s):  
Learning Strategies ◽  
Implementation Strategies ◽  
Qualitative Evaluation ◽  
Evaluation Process ◽  
Product Evaluation ◽  
Education Management ◽  
Evaluation Approach ◽  
Process Learning ◽  
Research Findings ◽  
Goals And Objectives

This study aims to determine (1) The results of the input evaluation; (2) The results of the evaluation process; and (3) The results of the product evaluation on the policy implementation of Act 12 of the year 2012 on the Program Study of Education Management, Postgraduate Program, University State of Jakarta (UNJ). The research method was a qualitative evaluation approach. Data collected by conducting an analysis of document-based curriculum KKNI to determine the success of the implementation by stages conducted at Postgraduate Program Study of Education Management. The data were analysed descriptively and meaning on any research findings conducted qualitatively. Stages of meaning carried out through the following stages: (1) Data Collection; (2) Data Reduction; and (3) Data Display. Based on the results of input evaluation which performed on the curriculum document, known that the preparation Program Study based on curriculum KKNI of Education Management Postgraduate Program is equipped with a clear legal basis, the formulation of goals and objectives. In the aspect of supporting resources for curriculum development, Progam Study has form data analysis of curriculum results, planned programs and implementation strategies. In the process, learning strategies are divided into two approaches, namely direct and indirect approaches. In evaluating, the results are more geared towards the achievement of the program on implementation of policies based on curriculum KKNI in Postgraduate Program Study of Education Management by a predetermined time phase, namely the achievement during the short-term period (1 to 2 years).

scholarly journals APPLICATIONS OF COMPUTER VISION IN AGRICULTURE

2021 ◽  
Vol 5 (9) ◽  
Author(s):  
Pranav Ghadge ◽  
Riddhik Tilawat ◽  
Prasanna Sand ◽  
Parul Jadhav
Keyword(s):  
Artificial Intelligence ◽  
Remote Sensing ◽  
Computer Vision ◽  
Educational Technology ◽  
Satellite System ◽  
High Quality ◽  
Agricultural Applications ◽  
Efficient Processing

Satellite system advances, remote sensing and drone technology are continuing. These progresses produce high-quality images that need efficient processing for smart agricultural applications. These possibilities to merge computer vision and artificial intelligence in agriculture are exploited with recent deep educational technology. This involves essential phenomena of data and huge quantities of data stored, analysed and used when making decisions. This paper demonstrates how computer vision in agriculture can be used.

Curve and Surface Fitting Techniques in Computer Vision

2021 ◽  
pp. 2150041
Author(s):  
Kyle Brown ◽  
Nikolaos Bourbakis
Keyword(s):  
Computer Vision ◽  
Surface Fitting ◽  
Multiple Features ◽  
Broad Applicability ◽  
Evaluation Approach ◽  
Curve And Surface Fitting ◽  
Robust To Noise

Curve and surface-fitting are classic problems of approximation that find use in many fields, including computer vision. There are two broad approaches to the problem — interpolation, which seeks to fit points exactly, and regression, which seeks a rougher approximation which is more robust to noise. This survey looks at several techniques of both kinds, with a particular focus on applications in computer vision. We make use of an empirical first-level evaluation approach which scores the techniques on multiple features based on how important they are to users of the technique and developers. This provides a quick summary of the broad applicability of the technique to most situations, rather than a deep evaluation of the performance and accuracy of the technique obtained by running it on several datasets.

Face Recognition with Active Appearance Model (AAM)

2013 ◽  
pp. 1124-1144 ◽  
Author(s):  
Patrycia Barros de Lima Klavdianos ◽  
Lourdes Mattos Brasil ◽  
Jairo Simão Santana Melo
Keyword(s):  
Artificial Intelligence ◽  
Computer Vision ◽  
Face Recognition ◽  
Face Processing ◽  
Research Field ◽  
Appearance Model ◽  
Active Appearance Model ◽  
Human Face ◽  
Active Appearance ◽  
Human Faces

Recognition of human faces has been a fascinating subject in research field for many years. It is considered a multidisciplinary field because it includes understanding different domains such as psychology, neuroscience, computer vision, artificial intelligence, mathematics, and many others. Human face perception is intriguing and draws our attention because we accomplish the task so well that we hope to one day witness a machine performing the same task in a similar or better way. This chapter aims to provide a systematic and practical approach regarding to one of the most current techniques applied on face recognition, known as AAM (Active Appearance Model). AAM method is addressed considering 2D face processing only. This chapter doesn’t cover the entire theme, but offers to the reader the necessary tools to construct a consistent and productive pathway toward this involving subject.

Sign in / Sign up

Export Citation Format

Share Document