1. The document discusses a method for detecting distracted drivers using computer vision and machine learning techniques. It proposes using a convolutional neural network (CNN), specifically modifying the VGG-16 architecture, to classify images and identify different types of driver distractions or safe driving behaviors.
2. The CNN would take images of the driver as input to extract features, which would then be classified by the network to determine if the driver is distracted or driving safely. The researchers evaluated their proposed system using the StateFarm distracted driver detection dataset.
3. Previous work on detecting distracted driving is discussed, including using features like hands, face, and mouth to identify cell phone use, as well as developing datasets and classifiers to detect other dist
APPLICATION OF VARIOUS DEEP LEARNING MODELS FOR AUTOMATIC TRAFFIC VIOLATION D...ijitcs
A rapid growth in the population and economic growth has resulted in an increasing number of vehicles on
road every year. Traffic congestion is a big problem in every metropolitan city. To reach their destination
faster and to avoid traffic, some people are violating traffic rules and regulations. Violation of traffic rules
puts everyone in danger. Maintaining traffic rules manually has become difficult over the time due to the
rapid increase in the population. This alarming situation has be taken care of at the earliest. To overcome
this, we need a real-time violation detection system to help maintain the traffic rules. The approach is to
detect traffic violations in real-time using edge computing, which reduces the time to detect. Different
machine learning models and algorithms were applied to detect traffic violations like traveling without a
helmet, line crossing, parking violation detection, violating the one-way rule etc. The model implemented
gave an accuracy of around 85%, due to memory constraints of the edge device in this case NVIDIA Jetson
Nano, as the fps is quite low.
A Review: Machine vision and its ApplicationsIOSR Journals
Abstract:The machine vision has been used in the industrial machine designing by using the intelligent character recognition. Due to its increased use, it makes the significant contribution to ensure the competitiveness in modern development. The state of art in machine vision inspection and a critical overview of applications in various industries are presented in this paper. In its restricted sense it is also known as the computer vision or the robot vision. This paper gives the overview of Machine Vision Technology in the first section, followed by various industrial application and thefuture trends in Machine Vision. Keywords:CCD- charged coupled devices, Fruit harvesting system, HIS- Hue Saturation Intensity, Image analysis, Image enhancement, Image feature extraction, Image feature classification processing, Intelligent Vehicle tracking , Isodiscriminationn Contour, Machine Vision
A Traffic Sign Classifier Model using Sage Makerijtsrd
Driver assistance technologies that relieve the drivers task, as well as intelligent autonomous vehicles, rely on traffic sign recognition. Normally the classification of traffic signs is a critical challenge for self driving cars. For the classification of traffic sign images, a Deep Network known as LeNet will be used in this study. There are forty three different categories of images in the dataset. There are two aspects to this structure Traffic sign identification and Traffic sign classification. ADASs are designed to perform a variety of tasks, including communications, detection of road markings, recognition of road signs, and detection of pedestrians. There are two aspects to this structure Traffic sign identification and Traffic sign classification. In the methodologies for detecting and recognizing traffic signals various techniques, such as colour segmentation and the RGB to HSI model area unit, were applied for traffic sign detection and recognition. Different elements contribute to recognition of HOG. Arpit Seth | Vijayakumar A "A Traffic Sign Classifier Model using Sage Maker" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd42411.pdf Paper URL: https://www.ijtsrd.comcomputer-science/artificial-intelligence/42411/a-traffic-sign-classifier-model-using-sage-maker/arpit-seth
An Improved Tracking Using IMU and Vision Fusion for Mobile Augmented Reality...ijma
Mobile Augmented Reality (MAR) is becoming an important cyber-physical system application given the
ubiquitous availability of mobile phones. With the need to operate in unprepared environments, accurate
and robust registration and tracking has become an important research problem to solve. In fact, when
MAR is used for tele-interactive applications involving large distances, say from an accident site to
insurance office, tracking at both the ends is desirable and further it is essential to appropriately fuse
inertial and vision sensors’ data. In this paper, we present results and discuss some insights gained in
marker-less tracking during the development of a prototype pertaining to an example use case related to
breakdown/damage assessment of a vehicle. The novelty of this paper is in bringing together different
components and modules with appropriate enhancements towards a complete working system.
APPLICATION OF VARIOUS DEEP LEARNING MODELS FOR AUTOMATIC TRAFFIC VIOLATION D...ijitcs
A rapid growth in the population and economic growth has resulted in an increasing number of vehicles on
road every year. Traffic congestion is a big problem in every metropolitan city. To reach their destination
faster and to avoid traffic, some people are violating traffic rules and regulations. Violation of traffic rules
puts everyone in danger. Maintaining traffic rules manually has become difficult over the time due to the
rapid increase in the population. This alarming situation has be taken care of at the earliest. To overcome
this, we need a real-time violation detection system to help maintain the traffic rules. The approach is to
detect traffic violations in real-time using edge computing, which reduces the time to detect. Different
machine learning models and algorithms were applied to detect traffic violations like traveling without a
helmet, line crossing, parking violation detection, violating the one-way rule etc. The model implemented
gave an accuracy of around 85%, due to memory constraints of the edge device in this case NVIDIA Jetson
Nano, as the fps is quite low.
A Review: Machine vision and its ApplicationsIOSR Journals
Abstract:The machine vision has been used in the industrial machine designing by using the intelligent character recognition. Due to its increased use, it makes the significant contribution to ensure the competitiveness in modern development. The state of art in machine vision inspection and a critical overview of applications in various industries are presented in this paper. In its restricted sense it is also known as the computer vision or the robot vision. This paper gives the overview of Machine Vision Technology in the first section, followed by various industrial application and thefuture trends in Machine Vision. Keywords:CCD- charged coupled devices, Fruit harvesting system, HIS- Hue Saturation Intensity, Image analysis, Image enhancement, Image feature extraction, Image feature classification processing, Intelligent Vehicle tracking , Isodiscriminationn Contour, Machine Vision
A Traffic Sign Classifier Model using Sage Makerijtsrd
Driver assistance technologies that relieve the drivers task, as well as intelligent autonomous vehicles, rely on traffic sign recognition. Normally the classification of traffic signs is a critical challenge for self driving cars. For the classification of traffic sign images, a Deep Network known as LeNet will be used in this study. There are forty three different categories of images in the dataset. There are two aspects to this structure Traffic sign identification and Traffic sign classification. ADASs are designed to perform a variety of tasks, including communications, detection of road markings, recognition of road signs, and detection of pedestrians. There are two aspects to this structure Traffic sign identification and Traffic sign classification. In the methodologies for detecting and recognizing traffic signals various techniques, such as colour segmentation and the RGB to HSI model area unit, were applied for traffic sign detection and recognition. Different elements contribute to recognition of HOG. Arpit Seth | Vijayakumar A "A Traffic Sign Classifier Model using Sage Maker" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd42411.pdf Paper URL: https://www.ijtsrd.comcomputer-science/artificial-intelligence/42411/a-traffic-sign-classifier-model-using-sage-maker/arpit-seth
An Improved Tracking Using IMU and Vision Fusion for Mobile Augmented Reality...ijma
Mobile Augmented Reality (MAR) is becoming an important cyber-physical system application given the
ubiquitous availability of mobile phones. With the need to operate in unprepared environments, accurate
and robust registration and tracking has become an important research problem to solve. In fact, when
MAR is used for tele-interactive applications involving large distances, say from an accident site to
insurance office, tracking at both the ends is desirable and further it is essential to appropriately fuse
inertial and vision sensors’ data. In this paper, we present results and discuss some insights gained in
marker-less tracking during the development of a prototype pertaining to an example use case related to
breakdown/damage assessment of a vehicle. The novelty of this paper is in bringing together different
components and modules with appropriate enhancements towards a complete working system.
A review on techniques and modelling methodologies used for checking electrom...nooriasukmaningtyas
The proper function of the integrated circuit (IC) in an inhibiting electromagnetic environment has always been a serious concern throughout the decades of revolution in the world of electronics, from disjunct devices to today’s integrated circuit technology, where billions of transistors are combined on a single chip. The automotive industry and smart vehicles in particular, are confronting design issues such as being prone to electromagnetic interference (EMI). Electronic control devices calculate incorrect outputs because of EMI and sensors give misleading values which can prove fatal in case of automotives. In this paper, the authors have non exhaustively tried to review research work concerned with the investigation of EMI in ICs and prediction of this EMI using various modelling methodologies and measurement setups.
Literature Review Basics and Understanding Reference Management.pptxDr Ramhari Poudyal
Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
Online aptitude test management system project report.pdfKamal Acharya
The purpose of on-line aptitude test system is to take online test in an efficient manner and no time wasting for checking the paper. The main objective of on-line aptitude test system is to efficiently evaluate the candidate thoroughly through a fully automated system that not only saves lot of time but also gives fast results. For students they give papers according to their convenience and time and there is no need of using extra thing like paper, pen etc. This can be used in educational institutions as well as in corporate world. Can be used anywhere any time as it is a web based application (user Location doesn’t matter). No restriction that examiner has to be present when the candidate takes the test.
Every time when lecturers/professors need to conduct examinations they have to sit down think about the questions and then create a whole new set of questions for each and every exam. In some cases the professor may want to give an open book online exam that is the student can take the exam any time anywhere, but the student might have to answer the questions in a limited time period. The professor may want to change the sequence of questions for every student. The problem that a student has is whenever a date for the exam is declared the student has to take it and there is no way he can take it at some other time. This project will create an interface for the examiner to create and store questions in a repository. It will also create an interface for the student to take examinations at his convenience and the questions and/or exams may be timed. Thereby creating an application which can be used by examiners and examinee’s simultaneously.
Examination System is very useful for Teachers/Professors. As in the teaching profession, you are responsible for writing question papers. In the conventional method, you write the question paper on paper, keep question papers separate from answers and all this information you have to keep in a locker to avoid unauthorized access. Using the Examination System you can create a question paper and everything will be written to a single exam file in encrypted format. You can set the General and Administrator password to avoid unauthorized access to your question paper. Every time you start the examination, the program shuffles all the questions and selects them randomly from the database, which reduces the chances of memorizing the questions.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.