This document presents a social distancing monitoring system that uses OpenCV and a YOLO object detection model to monitor social distancing in public areas. The system processes video frames to detect people and calculate the distance between them. It will sound an alert if the distance between any two people is less than the configured social distancing limit. The system is designed to be installed in locations with moderate crowd sizes like banks, ATMs, shops, and offices to help enforce social distancing and reduce the spread of COVID-19 and other diseases. It works by loading a pre-trained YOLO model, detecting people in frames, finding their centroids, and calculating distances to identify violations of the social distancing parameter.
This document describes a social distance detection system to help identify areas with high violations of COVID-19 guidelines. The system uses YOLO v3 for object detection, OpenCV for distance calculation, and a mask classifier to differentiate humans and detect social distancing violations and lack of masks. It records violations to predict disease hotspots. The system was able to accurately detect humans, measure distances between them, and identify situations that violate social distancing or lack of mask guidelines to help control the spread of contagious diseases like COVID-19.
NEW CORONA VIRUS DISEASE 2022: SOCIAL DISTANCING IS AN EFFECTIVE MEASURE (COV...IRJET Journal
The document describes a proposed real-time system to monitor social distancing using computer vision and deep learning techniques. The system would use a camera to detect individuals and calculate distances between them in order to identify instances where social distancing guidelines are breached. When a breach is detected, an audio-visual cue would be emitted to alert individuals without identifying or saving personal data. The system aims to help reduce the spread of COVID-19 while respecting privacy and avoiding overreach. It outlines the technical approach including camera calibration, region of interest definition, object detection using YOLOv3, distance calculation techniques, and system architecture at a high level.
Social Distance Detector Using Computer Vision, OpenCV and YOLO Deep Learning...IRJET Journal
This paper presents a method to detect social distancing using computer vision, OpenCV and the YOLO deep learning algorithm. The proposed system takes in a video, detects people using YOLO object detection, calculates the distance between detected people by finding the centroid of bounding boxes, and determines if distances are less than 1 meter to identify social distancing violations. Using CUDA allows the model to leverage GPU processing power for faster and more accurate real-time results compared to CPU-only methods. The system is able to monitor people and detect whether social distancing guidelines are being followed to help control the spread of COVID-19.
Real Time Social Distance Detector using Deep learningIRJET Journal
The document describes a real-time social distance detector using deep learning. The researchers created a model called SocialdistancingNet-19 that can identify people in frames from video and label them as safe or dangerous based on whether they are more than a certain distance threshold from others. They used a pre-trained YOLO v3 object detection model to detect people and then calculated the distance between centroids of detected objects to assess social distancing compliance. The model achieved 92.8% accuracy on test data. It is intended to automatically monitor social distancing in public spaces using video surveillance to help reduce the spread of COVID-19.
Designing of Application for Detection of Face Mask and Social Distancing Dur...IRJET Journal
This document proposes a system to detect whether people are wearing face masks and maintaining social distancing during the COVID-19 pandemic using computer vision algorithms. The system uses YOLO v3 for object detection to detect people and faces in frames. A CNN model is then used to classify whether faces are wearing masks or not. Social distancing is measured by calculating the Euclidean distance between detected face boxes. The system is intended to help enforce COVID safety protocols and reduce cases by automatically monitoring compliance. It analyzes video frames to label faces as masked or unmasked and issue notifications if people are too close. The proposed application aims to assist governments in controlling the pandemic through machine learning-based social distancing and mask detection.
Covid Face Mask Detection Using Neural NetworksIRJET Journal
The document describes a study that developed a convolutional neural network (CNN) model using the MobileNetV2 architecture to detect if people in images are wearing face masks properly, improperly, or not at all. The model was trained on a dataset containing these three classes and achieved an accuracy of 97.25% for classifying images. The developed model can be implemented in real-world applications like public transportation stations, hospitals, offices, and schools to help monitor mask compliance and reduce the spread of COVID-19.
Social Distance Monitoring and Mask Detection Using Deep Learning TechniquesIRJET Journal
The document describes a proposed system to monitor social distancing using computer vision and deep learning techniques. The system uses the YOLOv5 object detection model to identify people in video feeds from surveillance cameras. It then analyzes the distances between detected individuals to determine if they are following social distancing guidelines. Image preprocessing and feature extraction techniques are used to improve detection accuracy. The system is intended to help enforce social distancing protocols during the COVID-19 pandemic and reduce disease transmission.
Face Mask Detection and Contactless Body Temperature SensingIRJET Journal
This document presents a proposed system to detect if individuals are wearing face masks and measure their body temperature using CCTV cameras at grocery stores. The system aims to help reduce the spread of COVID-19 by notifying store owners if an unmasked individual or someone with a high temperature is detected. It uses computer vision and deep learning techniques like convolutional neural networks to identify faces and determine if a mask is worn correctly. An infrared thermometer would also measure temperatures and alert authorities by email if thresholds are exceeded. The researchers hope this system can be implemented in other public areas to enforce mask and distancing rules and control viral transmission through early detection of potential cases.
This document describes a social distance detection system to help identify areas with high violations of COVID-19 guidelines. The system uses YOLO v3 for object detection, OpenCV for distance calculation, and a mask classifier to differentiate humans and detect social distancing violations and lack of masks. It records violations to predict disease hotspots. The system was able to accurately detect humans, measure distances between them, and identify situations that violate social distancing or lack of mask guidelines to help control the spread of contagious diseases like COVID-19.
NEW CORONA VIRUS DISEASE 2022: SOCIAL DISTANCING IS AN EFFECTIVE MEASURE (COV...IRJET Journal
The document describes a proposed real-time system to monitor social distancing using computer vision and deep learning techniques. The system would use a camera to detect individuals and calculate distances between them in order to identify instances where social distancing guidelines are breached. When a breach is detected, an audio-visual cue would be emitted to alert individuals without identifying or saving personal data. The system aims to help reduce the spread of COVID-19 while respecting privacy and avoiding overreach. It outlines the technical approach including camera calibration, region of interest definition, object detection using YOLOv3, distance calculation techniques, and system architecture at a high level.
Social Distance Detector Using Computer Vision, OpenCV and YOLO Deep Learning...IRJET Journal
This paper presents a method to detect social distancing using computer vision, OpenCV and the YOLO deep learning algorithm. The proposed system takes in a video, detects people using YOLO object detection, calculates the distance between detected people by finding the centroid of bounding boxes, and determines if distances are less than 1 meter to identify social distancing violations. Using CUDA allows the model to leverage GPU processing power for faster and more accurate real-time results compared to CPU-only methods. The system is able to monitor people and detect whether social distancing guidelines are being followed to help control the spread of COVID-19.
Real Time Social Distance Detector using Deep learningIRJET Journal
The document describes a real-time social distance detector using deep learning. The researchers created a model called SocialdistancingNet-19 that can identify people in frames from video and label them as safe or dangerous based on whether they are more than a certain distance threshold from others. They used a pre-trained YOLO v3 object detection model to detect people and then calculated the distance between centroids of detected objects to assess social distancing compliance. The model achieved 92.8% accuracy on test data. It is intended to automatically monitor social distancing in public spaces using video surveillance to help reduce the spread of COVID-19.
Designing of Application for Detection of Face Mask and Social Distancing Dur...IRJET Journal
This document proposes a system to detect whether people are wearing face masks and maintaining social distancing during the COVID-19 pandemic using computer vision algorithms. The system uses YOLO v3 for object detection to detect people and faces in frames. A CNN model is then used to classify whether faces are wearing masks or not. Social distancing is measured by calculating the Euclidean distance between detected face boxes. The system is intended to help enforce COVID safety protocols and reduce cases by automatically monitoring compliance. It analyzes video frames to label faces as masked or unmasked and issue notifications if people are too close. The proposed application aims to assist governments in controlling the pandemic through machine learning-based social distancing and mask detection.
Covid Face Mask Detection Using Neural NetworksIRJET Journal
The document describes a study that developed a convolutional neural network (CNN) model using the MobileNetV2 architecture to detect if people in images are wearing face masks properly, improperly, or not at all. The model was trained on a dataset containing these three classes and achieved an accuracy of 97.25% for classifying images. The developed model can be implemented in real-world applications like public transportation stations, hospitals, offices, and schools to help monitor mask compliance and reduce the spread of COVID-19.
Social Distance Monitoring and Mask Detection Using Deep Learning TechniquesIRJET Journal
The document describes a proposed system to monitor social distancing using computer vision and deep learning techniques. The system uses the YOLOv5 object detection model to identify people in video feeds from surveillance cameras. It then analyzes the distances between detected individuals to determine if they are following social distancing guidelines. Image preprocessing and feature extraction techniques are used to improve detection accuracy. The system is intended to help enforce social distancing protocols during the COVID-19 pandemic and reduce disease transmission.
Face Mask Detection and Contactless Body Temperature SensingIRJET Journal
This document presents a proposed system to detect if individuals are wearing face masks and measure their body temperature using CCTV cameras at grocery stores. The system aims to help reduce the spread of COVID-19 by notifying store owners if an unmasked individual or someone with a high temperature is detected. It uses computer vision and deep learning techniques like convolutional neural networks to identify faces and determine if a mask is worn correctly. An infrared thermometer would also measure temperatures and alert authorities by email if thresholds are exceeded. The researchers hope this system can be implemented in other public areas to enforce mask and distancing rules and control viral transmission through early detection of potential cases.
Face Mask and Social Distance DetectionIRJET Journal
1) The document describes a project that uses computer vision techniques like convolutional neural networks and YOLO to detect face masks and social distancing in video feeds.
2) It trains models using OpenCV, TensorFlow and Keras to identify if people in frames are wearing masks or not, and to check if social distancing protocols are being followed.
3) The system is meant to help enforce COVID safety protocols at locations like schools, businesses and public transit by monitoring mask usage and physical distancing.
A Survey on Person Detection for Social Distancing and Safety Violation Alert...IRJET Journal
This document discusses methods for monitoring social distancing using video surveillance and deep learning techniques. It describes how faster R-CNN, single shot detector (SSD) and YOLO v3 deep learning models can be used to detect people in video frames and calculate the distance between individuals to determine if social distancing guidelines are being followed. If distances between people are found to be unsafe, the system can send alerts or cautions. The methodology is intended to help prevent the spread of COVID-19 by monitoring adherence to social distancing and triggering warnings if safety violations are detected.
Covid Mask Detection and Social Distancing Using Raspberry piIRJET Journal
This document describes a system that uses computer vision and machine learning to detect if individuals are wearing masks and maintaining proper social distancing in public places. The system uses a Raspberry Pi connected to a USB camera to take photos and video. Convolutional neural network models like CNN and YOLO are used to analyze the images, detect faces, and determine if masks are being worn correctly. If individuals are not wearing masks or social distancing, the system will provide an alert or sound from a connected speaker. The goal is to help enforce mask and distancing guidelines without needing human monitoring, in order to reduce virus spread during the COVID-19 pandemic.
This document summarizes a research paper on developing a system to detect whether individuals are wearing face masks using CCTV cameras in public places like grocery stores. The system uses convolutional neural networks (CNN) for face detection and mask detection in images from the cameras. If someone is detected without a mask, an alert is sent to store owners. The goal is to help reduce the spread of COVID-19 by enforcing mask rules and making people aware of the importance of masks for health and safety. The proposed system could be expanded for use in other public areas like malls and universities to monitor mask compliance through IoT-connected cameras.
Social Distancing Detector Management SystemIRJET Journal
This document describes a social distancing detector management system that uses computer vision and deep learning techniques. It aims to detect distances between people and warn them if they are not maintaining the recommended 6 feet distance, in order to help reduce the spread of COVID-19. The system uses a Raspberry Pi, OpenCV for image processing, and the YOLOv3 deep learning model trained on object detection. It works by detecting people in images or video frames using the YOLOv3 model, then calculates pixel distances between detected people and compares to a threshold to identify social distancing violations. The goal is to monitor social distancing and alert people to help slow the pandemic in places like schools, offices, and other areas where groups
Covid-19 Detection Using Deep Neural NetworksIRJET Journal
This document describes a study that developed a deep neural network model to detect COVID-19 using symptoms and patient data. The researchers collected data on symptoms and information from COVID-19 positive and negative patients. They trained a neural network model on this data to predict the probability a person is infected. The model achieved 65% accuracy but could be improved with more data. It was integrated into a web application where users can input symptoms and receive a prediction. The researchers conclude the model has potential to enable early detection and reduce spread of COVID-19 if improved with additional data and techniques like hyperparameter tuning.
Deep Learning Assisted Tool for Face Mask DetectionIRJET Journal
This document presents a deep learning model for face mask detection. The model was developed using TensorFlow and Keras with a MobileNetV2 architecture. It involves collecting a dataset of images with and without masks, preprocessing the data, training a classifier to identify masks, and applying the trained model to detect masks in real-time video. The model analyzes each detected face and places a colored box around it to indicate if a mask is detected or not. Evaluation on test data showed the model achieved accurate mask detection from video streams in real-time.
1) The document describes an Android app called 6'Apart that uses object detection and machine learning to analyze videos taken in crowded areas and determine if people are maintaining proper social distancing of 6 feet or more.
2) The app uses a pre-trained YOLOv3 model to detect objects and people in videos recorded by users. It then calculates the centroids of detected people and checks the distance between centroids to analyze social distancing compliance.
3) The goal is to help police and individuals determine whether an area is following social distancing guidelines as lockdowns are lifted during the COVID-19 pandemic.
1) The document describes an Android app called 6'Apart that uses object detection and machine learning to analyze videos taken in crowded areas and determine if people are maintaining proper social distancing of 6 feet or more.
2) The app uses a pre-trained YOLOv3 model to detect objects and people in videos recorded by users. It then calculates the centroids of detected people and checks the distance between centroids to analyze social distancing compliance.
3) The goal is to help police and individuals determine whether an area is following social distancing guidelines as lockdowns are lifted during the COVID-19 pandemic.
AI-based Mechanism to Authorise Beneficiaries at Covid Vaccination Camps usin...IRJET Journal
The document presents a research on developing an AI-based facial recognition system to authorize beneficiaries at COVID-19 vaccination camps. It aims to create a deep learning model that allows individuals to register for vaccinations and book slots using real-time face recognition. This aims to make the process contactless and reduce infection risk at camps. The proposed system uses a CNN model that extracts facial features from images to encode them as hashes for identification. It achieved 98.34% accuracy in tests, making it effective for replacing identification methods requiring physical documents or contact. The system could help address issues like de-duplication of beneficiaries and ensuring compliance with safety protocols at crowded camps.
PREDICTION OF COVID-19 USING MACHINE LEARNING APPROACHESIRJET Journal
This document summarizes a research paper that used machine learning models to predict the spread of COVID-19. The researchers used various machine learning algorithms like SVM, random forest, decision tree, and linear regression on COVID-19 case data. SVM had the highest error in predictions, while random forest and decision tree performed best with lowest error. The models were developed using Python and deployed on cloud platforms. The study aimed to accurately predict COVID-19 trends to help governments respond better to the pandemic.
This document proposes a vision-based approach to detect violations of social distancing using computer vision algorithms. The approach uses inverse perspective mapping to transform frames from surveillance cameras into a bird's eye view representation with real-world coordinates. It then applies Gaussian mixture modeling for background subtraction, Kalman filtering for tracking, and distance calculations to identify instances where two individuals are within 2 meters and therefore not socially distanced. The results show the approach can accurately detect social distancing violations in different scenarios.
This document describes a project to detect fake news and messages using machine learning techniques. The goal is to classify news stories and messages as real or fake, and check the validity of websites publishing the news. The project will use concepts from artificial intelligence, natural language processing, and machine learning, including logistic regression, decision trees, gradient boosting, and random forests. The system is intended as a web application that provides guidance to users on identifying fake news and messages spread on social media platforms. It will analyze data from online sources to identify main keywords and topics associated with fake content. Tips will also be provided on how to prevent the spread of misinformation.
Covid-19 Data Analysis and VisualizationIRJET Journal
This document summarizes a research paper that analyzes COVID-19 data using machine learning algorithms. It first introduces the authors and provides an abstract describing the project's goal of gaining insights from COVID-19 data using Python and Tableau visualization tools. It then reviews related work applying models and algorithms to infectious disease data. The methodology section outlines the process used: collecting data from government websites, cleaning the data, performing data visualization, calculating accuracy of different algorithms (logistic regression, KNN, random forest, decision tree), and using the most accurate algorithm to predict if a person is COVID-19 positive based on symptoms.
Automatic covid screening and deep learningIRJET Journal
This document discusses the development of an IoT-based smart screening and disinfection system to help control the spread of COVID-19. The proposed system uses temperature sensors, a pulse sensor, and image processing with deep learning for face mask detection to screen individuals. If thresholds are exceeded, the system would record the individual and activate disinfection protocols like spraying sanitizer or closing doors. It was designed to reduce human involvement in screening processes. The system aims to minimize COVID-19 spread at public entrances through rapid, automated screening and recorded tracking of suspected cases.
Survey on Face Mask Detection with Door Locking and Alert System using Raspbe...IRJET Journal
1) The researchers developed a face mask detection system using Raspberry Pi, CNN, and other techniques to help prevent the spread of COVID-19.
2) The system detects faces in video streams to identify if a person is wearing a mask or not. If someone is not wearing a mask, an alert is triggered through a buzzer and the door will not open.
3) The system was trained on datasets containing images of people with and without masks and uses CNNs, TensorFlow, and other deep learning methods for detection and classification.
Realtime Face mask Detector using YoloV4IRJET Journal
This document presents a real-time face mask detector using YOLOv4. The system was able to detect faces with 94.75% accuracy and a maximum frame rate of 38 FPS. It used a dataset of images with bounding box annotations that was split into training and validation sets. The YOLOv4 model was trained on the darknet framework using this dataset. Testing showed it could accurately detect single people with or without masks and multiple people in various scenarios. The authors conclude the model provides fast and accurate mask detection in real-time suitable for applications like monitoring mask compliance.
The document describes a social distancing detection system that uses the YOLO object detection algorithm and COCO dataset to detect people in video frames and estimate distances between them. It draws bounding boxes around detected people, with violations of the default distance threshold shown in red and non-violations in green. The number of violations and alert messages are displayed on screen to help users maintain safe distances. The system was tested on prerecorded video and images and was able to accurately detect violations and maintain social distancing.
Face Mask Detection utilizing Tensorflow, OpenCV and KerasIRJET Journal
This document describes a face mask detection system created using computer vision and deep learning techniques. The system uses OpenCV for image preprocessing, TensorFlow for creating and training a convolutional neural network (CNN) model, and Keras as the API for model definition and training. The CNN is trained on datasets containing images of faces with and without masks. It achieves 95.77% accuracy on one dataset and 94.58% accuracy on a more challenging dataset. When deployed, the trained model is able to detect and label faces in real-time video frames as wearing a mask or not wearing a mask, helping to monitor mask compliance and reduce disease spread.
A Novel Method For Evaluation of Automation Dry Fog Disinfecting UnitIRJET Journal
This document presents a novel method for evaluating an automated dry fog disinfecting unit. The COVID-19 pandemic has increased interest in automation robots to conduct work in contaminated areas safely. The paper describes the design and development of a new affordable autonomous indoor sterilization robot that uses a wheeled mobile platform and hydrogen peroxide fogging device. A simulation analysis of the dry mist hydrogen peroxide sterilization model was conducted to study dispersal in an indoor environment. The efficacy of the created robot was tested in practical situations like hospitals, hotels, offices and laboratories, with positive results confirmed by an independent testing organization. The robot is aimed at autonomous indoor sanitization tasks to reduce human exposure to pathogens.
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...IRJET Journal
1) The document discusses the Sungal Tunnel project in Jammu and Kashmir, India, which is being constructed using the New Austrian Tunneling Method (NATM).
2) NATM involves continuous monitoring during construction to adapt to changing ground conditions, and makes extensive use of shotcrete for temporary tunnel support.
3) The methodology section outlines the systematic geotechnical design process for tunnels according to Austrian guidelines, and describes the various steps of NATM tunnel construction including initial and secondary tunnel support.
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTUREIRJET Journal
This study examines the effect of response reduction factors (R factors) on reinforced concrete (RC) framed structures through nonlinear dynamic analysis. Three RC frame models with varying heights (4, 8, and 12 stories) were analyzed in ETABS software under different R factors ranging from 1 to 5. The results showed that displacement increased as the R factor decreased, indicating less linear behavior for lower R factors. Drift also decreased proportionally with increasing R factors from 1 to 5. Shear forces in the frames decreased with higher R factors. In general, R factors of 3 to 5 produced more satisfactory performance with less displacement and drift. The displacement variations between different building heights were consistent at different R factors. This study evaluated how R factors influence
More Related Content
Similar to A Social Distancing Monitoring System Using OpenCV to Ensure Social Distancing in Public Areas
Face Mask and Social Distance DetectionIRJET Journal
1) The document describes a project that uses computer vision techniques like convolutional neural networks and YOLO to detect face masks and social distancing in video feeds.
2) It trains models using OpenCV, TensorFlow and Keras to identify if people in frames are wearing masks or not, and to check if social distancing protocols are being followed.
3) The system is meant to help enforce COVID safety protocols at locations like schools, businesses and public transit by monitoring mask usage and physical distancing.
A Survey on Person Detection for Social Distancing and Safety Violation Alert...IRJET Journal
This document discusses methods for monitoring social distancing using video surveillance and deep learning techniques. It describes how faster R-CNN, single shot detector (SSD) and YOLO v3 deep learning models can be used to detect people in video frames and calculate the distance between individuals to determine if social distancing guidelines are being followed. If distances between people are found to be unsafe, the system can send alerts or cautions. The methodology is intended to help prevent the spread of COVID-19 by monitoring adherence to social distancing and triggering warnings if safety violations are detected.
Covid Mask Detection and Social Distancing Using Raspberry piIRJET Journal
This document describes a system that uses computer vision and machine learning to detect if individuals are wearing masks and maintaining proper social distancing in public places. The system uses a Raspberry Pi connected to a USB camera to take photos and video. Convolutional neural network models like CNN and YOLO are used to analyze the images, detect faces, and determine if masks are being worn correctly. If individuals are not wearing masks or social distancing, the system will provide an alert or sound from a connected speaker. The goal is to help enforce mask and distancing guidelines without needing human monitoring, in order to reduce virus spread during the COVID-19 pandemic.
This document summarizes a research paper on developing a system to detect whether individuals are wearing face masks using CCTV cameras in public places like grocery stores. The system uses convolutional neural networks (CNN) for face detection and mask detection in images from the cameras. If someone is detected without a mask, an alert is sent to store owners. The goal is to help reduce the spread of COVID-19 by enforcing mask rules and making people aware of the importance of masks for health and safety. The proposed system could be expanded for use in other public areas like malls and universities to monitor mask compliance through IoT-connected cameras.
Social Distancing Detector Management SystemIRJET Journal
This document describes a social distancing detector management system that uses computer vision and deep learning techniques. It aims to detect distances between people and warn them if they are not maintaining the recommended 6 feet distance, in order to help reduce the spread of COVID-19. The system uses a Raspberry Pi, OpenCV for image processing, and the YOLOv3 deep learning model trained on object detection. It works by detecting people in images or video frames using the YOLOv3 model, then calculates pixel distances between detected people and compares to a threshold to identify social distancing violations. The goal is to monitor social distancing and alert people to help slow the pandemic in places like schools, offices, and other areas where groups
Covid-19 Detection Using Deep Neural NetworksIRJET Journal
This document describes a study that developed a deep neural network model to detect COVID-19 using symptoms and patient data. The researchers collected data on symptoms and information from COVID-19 positive and negative patients. They trained a neural network model on this data to predict the probability a person is infected. The model achieved 65% accuracy but could be improved with more data. It was integrated into a web application where users can input symptoms and receive a prediction. The researchers conclude the model has potential to enable early detection and reduce spread of COVID-19 if improved with additional data and techniques like hyperparameter tuning.
Deep Learning Assisted Tool for Face Mask DetectionIRJET Journal
This document presents a deep learning model for face mask detection. The model was developed using TensorFlow and Keras with a MobileNetV2 architecture. It involves collecting a dataset of images with and without masks, preprocessing the data, training a classifier to identify masks, and applying the trained model to detect masks in real-time video. The model analyzes each detected face and places a colored box around it to indicate if a mask is detected or not. Evaluation on test data showed the model achieved accurate mask detection from video streams in real-time.
1) The document describes an Android app called 6'Apart that uses object detection and machine learning to analyze videos taken in crowded areas and determine if people are maintaining proper social distancing of 6 feet or more.
2) The app uses a pre-trained YOLOv3 model to detect objects and people in videos recorded by users. It then calculates the centroids of detected people and checks the distance between centroids to analyze social distancing compliance.
3) The goal is to help police and individuals determine whether an area is following social distancing guidelines as lockdowns are lifted during the COVID-19 pandemic.
1) The document describes an Android app called 6'Apart that uses object detection and machine learning to analyze videos taken in crowded areas and determine if people are maintaining proper social distancing of 6 feet or more.
2) The app uses a pre-trained YOLOv3 model to detect objects and people in videos recorded by users. It then calculates the centroids of detected people and checks the distance between centroids to analyze social distancing compliance.
3) The goal is to help police and individuals determine whether an area is following social distancing guidelines as lockdowns are lifted during the COVID-19 pandemic.
AI-based Mechanism to Authorise Beneficiaries at Covid Vaccination Camps usin...IRJET Journal
The document presents a research on developing an AI-based facial recognition system to authorize beneficiaries at COVID-19 vaccination camps. It aims to create a deep learning model that allows individuals to register for vaccinations and book slots using real-time face recognition. This aims to make the process contactless and reduce infection risk at camps. The proposed system uses a CNN model that extracts facial features from images to encode them as hashes for identification. It achieved 98.34% accuracy in tests, making it effective for replacing identification methods requiring physical documents or contact. The system could help address issues like de-duplication of beneficiaries and ensuring compliance with safety protocols at crowded camps.
PREDICTION OF COVID-19 USING MACHINE LEARNING APPROACHESIRJET Journal
This document summarizes a research paper that used machine learning models to predict the spread of COVID-19. The researchers used various machine learning algorithms like SVM, random forest, decision tree, and linear regression on COVID-19 case data. SVM had the highest error in predictions, while random forest and decision tree performed best with lowest error. The models were developed using Python and deployed on cloud platforms. The study aimed to accurately predict COVID-19 trends to help governments respond better to the pandemic.
This document proposes a vision-based approach to detect violations of social distancing using computer vision algorithms. The approach uses inverse perspective mapping to transform frames from surveillance cameras into a bird's eye view representation with real-world coordinates. It then applies Gaussian mixture modeling for background subtraction, Kalman filtering for tracking, and distance calculations to identify instances where two individuals are within 2 meters and therefore not socially distanced. The results show the approach can accurately detect social distancing violations in different scenarios.
This document describes a project to detect fake news and messages using machine learning techniques. The goal is to classify news stories and messages as real or fake, and check the validity of websites publishing the news. The project will use concepts from artificial intelligence, natural language processing, and machine learning, including logistic regression, decision trees, gradient boosting, and random forests. The system is intended as a web application that provides guidance to users on identifying fake news and messages spread on social media platforms. It will analyze data from online sources to identify main keywords and topics associated with fake content. Tips will also be provided on how to prevent the spread of misinformation.
Covid-19 Data Analysis and VisualizationIRJET Journal
This document summarizes a research paper that analyzes COVID-19 data using machine learning algorithms. It first introduces the authors and provides an abstract describing the project's goal of gaining insights from COVID-19 data using Python and Tableau visualization tools. It then reviews related work applying models and algorithms to infectious disease data. The methodology section outlines the process used: collecting data from government websites, cleaning the data, performing data visualization, calculating accuracy of different algorithms (logistic regression, KNN, random forest, decision tree), and using the most accurate algorithm to predict if a person is COVID-19 positive based on symptoms.
Automatic covid screening and deep learningIRJET Journal
This document discusses the development of an IoT-based smart screening and disinfection system to help control the spread of COVID-19. The proposed system uses temperature sensors, a pulse sensor, and image processing with deep learning for face mask detection to screen individuals. If thresholds are exceeded, the system would record the individual and activate disinfection protocols like spraying sanitizer or closing doors. It was designed to reduce human involvement in screening processes. The system aims to minimize COVID-19 spread at public entrances through rapid, automated screening and recorded tracking of suspected cases.
Survey on Face Mask Detection with Door Locking and Alert System using Raspbe...IRJET Journal
1) The researchers developed a face mask detection system using Raspberry Pi, CNN, and other techniques to help prevent the spread of COVID-19.
2) The system detects faces in video streams to identify if a person is wearing a mask or not. If someone is not wearing a mask, an alert is triggered through a buzzer and the door will not open.
3) The system was trained on datasets containing images of people with and without masks and uses CNNs, TensorFlow, and other deep learning methods for detection and classification.
Realtime Face mask Detector using YoloV4IRJET Journal
This document presents a real-time face mask detector using YOLOv4. The system was able to detect faces with 94.75% accuracy and a maximum frame rate of 38 FPS. It used a dataset of images with bounding box annotations that was split into training and validation sets. The YOLOv4 model was trained on the darknet framework using this dataset. Testing showed it could accurately detect single people with or without masks and multiple people in various scenarios. The authors conclude the model provides fast and accurate mask detection in real-time suitable for applications like monitoring mask compliance.
The document describes a social distancing detection system that uses the YOLO object detection algorithm and COCO dataset to detect people in video frames and estimate distances between them. It draws bounding boxes around detected people, with violations of the default distance threshold shown in red and non-violations in green. The number of violations and alert messages are displayed on screen to help users maintain safe distances. The system was tested on prerecorded video and images and was able to accurately detect violations and maintain social distancing.
Face Mask Detection utilizing Tensorflow, OpenCV and KerasIRJET Journal
This document describes a face mask detection system created using computer vision and deep learning techniques. The system uses OpenCV for image preprocessing, TensorFlow for creating and training a convolutional neural network (CNN) model, and Keras as the API for model definition and training. The CNN is trained on datasets containing images of faces with and without masks. It achieves 95.77% accuracy on one dataset and 94.58% accuracy on a more challenging dataset. When deployed, the trained model is able to detect and label faces in real-time video frames as wearing a mask or not wearing a mask, helping to monitor mask compliance and reduce disease spread.
A Novel Method For Evaluation of Automation Dry Fog Disinfecting UnitIRJET Journal
This document presents a novel method for evaluating an automated dry fog disinfecting unit. The COVID-19 pandemic has increased interest in automation robots to conduct work in contaminated areas safely. The paper describes the design and development of a new affordable autonomous indoor sterilization robot that uses a wheeled mobile platform and hydrogen peroxide fogging device. A simulation analysis of the dry mist hydrogen peroxide sterilization model was conducted to study dispersal in an indoor environment. The efficacy of the created robot was tested in practical situations like hospitals, hotels, offices and laboratories, with positive results confirmed by an independent testing organization. The robot is aimed at autonomous indoor sanitization tasks to reduce human exposure to pathogens.
Similar to A Social Distancing Monitoring System Using OpenCV to Ensure Social Distancing in Public Areas (20)
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...IRJET Journal
1) The document discusses the Sungal Tunnel project in Jammu and Kashmir, India, which is being constructed using the New Austrian Tunneling Method (NATM).
2) NATM involves continuous monitoring during construction to adapt to changing ground conditions, and makes extensive use of shotcrete for temporary tunnel support.
3) The methodology section outlines the systematic geotechnical design process for tunnels according to Austrian guidelines, and describes the various steps of NATM tunnel construction including initial and secondary tunnel support.
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTUREIRJET Journal
This study examines the effect of response reduction factors (R factors) on reinforced concrete (RC) framed structures through nonlinear dynamic analysis. Three RC frame models with varying heights (4, 8, and 12 stories) were analyzed in ETABS software under different R factors ranging from 1 to 5. The results showed that displacement increased as the R factor decreased, indicating less linear behavior for lower R factors. Drift also decreased proportionally with increasing R factors from 1 to 5. Shear forces in the frames decreased with higher R factors. In general, R factors of 3 to 5 produced more satisfactory performance with less displacement and drift. The displacement variations between different building heights were consistent at different R factors. This study evaluated how R factors influence
A COMPARATIVE ANALYSIS OF RCC ELEMENT OF SLAB WITH STARK STEEL (HYSD STEEL) A...IRJET Journal
This study compares the use of Stark Steel and TMT Steel as reinforcement materials in a two-way reinforced concrete slab. Mechanical testing is conducted to determine the tensile strength, yield strength, and other properties of each material. A two-way slab design adhering to codes and standards is executed with both materials. The performance is analyzed in terms of deflection, stability under loads, and displacement. Cost analyses accounting for material, durability, maintenance, and life cycle costs are also conducted. The findings provide insights into the economic and structural implications of each material for reinforcement selection and recommendations on the most suitable material based on the analysis.
Effect of Camber and Angles of Attack on Airfoil CharacteristicsIRJET Journal
This document discusses a study analyzing the effect of camber, position of camber, and angle of attack on the aerodynamic characteristics of airfoils. Sixteen modified asymmetric NACA airfoils were analyzed using computational fluid dynamics (CFD) by varying the camber, camber position, and angle of attack. The results showed the relationship between these parameters and the lift coefficient, drag coefficient, and lift to drag ratio. This provides insight into how changes in airfoil geometry impact aerodynamic performance.
A Review on the Progress and Challenges of Aluminum-Based Metal Matrix Compos...IRJET Journal
This document reviews the progress and challenges of aluminum-based metal matrix composites (MMCs), focusing on their fabrication processes and applications. It discusses how various aluminum MMCs have been developed using reinforcements like borides, carbides, oxides, and nitrides to improve mechanical and wear properties. These composites have gained prominence for their lightweight, high-strength and corrosion resistance properties. The document also examines recent advancements in fabrication techniques for aluminum MMCs and their growing applications in industries such as aerospace and automotive. However, it notes that challenges remain around issues like improper mixing of reinforcements and reducing reinforcement agglomeration.
Dynamic Urban Transit Optimization: A Graph Neural Network Approach for Real-...IRJET Journal
This document discusses research on using graph neural networks (GNNs) for dynamic optimization of public transportation networks in real-time. GNNs represent transit networks as graphs with nodes as stops and edges as connections. The GNN model aims to optimize networks using real-time data on vehicle locations, arrival times, and passenger loads. This helps increase mobility, decrease traffic, and improve efficiency. The system continuously trains and infers to adapt to changing transit conditions, providing decision support tools. While research has focused on performance, more work is needed on security, socio-economic impacts, contextual generalization of models, continuous learning approaches, and effective real-time visualization.
Structural Analysis and Design of Multi-Storey Symmetric and Asymmetric Shape...IRJET Journal
This document summarizes a research project that aims to compare the structural performance of conventional slab and grid slab systems in multi-story buildings using ETABS software. The study will analyze both symmetric and asymmetric building models under various loading conditions. Parameters like deflections, moments, shears, and stresses will be examined to evaluate the structural effectiveness of each slab type. The results will provide insights into the comparative behavior of conventional and grid slabs to help engineers and architects select appropriate slab systems based on building layouts and design requirements.
A Review of “Seismic Response of RC Structures Having Plan and Vertical Irreg...IRJET Journal
This document summarizes and reviews a research paper on the seismic response of reinforced concrete (RC) structures with plan and vertical irregularities, with and without infill walls. It discusses how infill walls can improve or reduce the seismic performance of RC buildings, depending on factors like wall layout, height distribution, connection to the frame, and relative stiffness of walls and frames. The reviewed research paper analyzes the behavior of infill walls, effects of vertical irregularities, and seismic performance of high-rise structures under linear static and dynamic analysis. It studies response characteristics like story drift, deflection and shear. The document also provides literature on similar research investigating the effects of infill walls, soft stories, plan irregularities, and different
This document provides a review of machine learning techniques used in Advanced Driver Assistance Systems (ADAS). It begins with an abstract that summarizes key applications of machine learning in ADAS, including object detection, recognition, and decision-making. The introduction discusses the integration of machine learning in ADAS and how it is transforming vehicle safety. The literature review then examines several research papers on topics like lightweight deep learning models for object detection and lane detection models using image processing. It concludes by discussing challenges and opportunities in the field, such as improving algorithm robustness and adaptability.
Long Term Trend Analysis of Precipitation and Temperature for Asosa district,...IRJET Journal
The document analyzes temperature and precipitation trends in Asosa District, Benishangul Gumuz Region, Ethiopia from 1993 to 2022 based on data from the local meteorological station. The results show:
1) The average maximum and minimum annual temperatures have generally decreased over time, with maximum temperatures decreasing by a factor of -0.0341 and minimum by -0.0152.
2) Mann-Kendall tests found the decreasing temperature trends to be statistically significant for annual maximum temperatures but not for annual minimum temperatures.
3) Annual precipitation in Asosa District showed a statistically significant increasing trend.
The conclusions recommend development planners account for rising summer precipitation and declining temperatures in
P.E.B. Framed Structure Design and Analysis Using STAAD ProIRJET Journal
This document discusses the design and analysis of pre-engineered building (PEB) framed structures using STAAD Pro software. It provides an overview of PEBs, including that they are designed off-site with building trusses and beams produced in a factory. STAAD Pro is identified as a key tool for modeling, analyzing, and designing PEBs to ensure their performance and safety under various load scenarios. The document outlines modeling structural parts in STAAD Pro, evaluating structural reactions, assigning loads, and following international design codes and standards. In summary, STAAD Pro is used to design and analyze PEB framed structures to ensure safety and code compliance.
A Review on Innovative Fiber Integration for Enhanced Reinforcement of Concre...IRJET Journal
This document provides a review of research on innovative fiber integration methods for reinforcing concrete structures. It discusses studies that have explored using carbon fiber reinforced polymer (CFRP) composites with recycled plastic aggregates to develop more sustainable strengthening techniques. It also examines using ultra-high performance fiber reinforced concrete to improve shear strength in beams. Additional topics covered include the dynamic responses of FRP-strengthened beams under static and impact loads, and the performance of preloaded CFRP-strengthened fiber reinforced concrete beams. The review highlights the potential of fiber composites to enable more sustainable and resilient construction practices.
Survey Paper on Cloud-Based Secured Healthcare SystemIRJET Journal
This document summarizes a survey on securing patient healthcare data in cloud-based systems. It discusses using technologies like facial recognition, smart cards, and cloud computing combined with strong encryption to securely store patient data. The survey found that healthcare professionals believe digitizing patient records and storing them in a centralized cloud system would improve access during emergencies and enable more efficient care compared to paper-based systems. However, ensuring privacy and security of patient data is paramount as healthcare incorporates these digital technologies.
Review on studies and research on widening of existing concrete bridgesIRJET Journal
This document summarizes several studies that have been conducted on widening existing concrete bridges. It describes a study from China that examined load distribution factors for a bridge widened with composite steel-concrete girders. It also outlines challenges and solutions for widening a bridge in the UAE, including replacing bearings and stitching the new and existing structures. Additionally, it discusses two bridge widening projects in New Zealand that involved adding precast beams and stitching to connect structures. Finally, safety measures and challenges for strengthening a historic bridge in Switzerland under live traffic are presented.
React based fullstack edtech web applicationIRJET Journal
The document describes the architecture of an educational technology web application built using the MERN stack. It discusses the frontend developed with ReactJS, backend with NodeJS and ExpressJS, and MongoDB database. The frontend provides dynamic user interfaces, while the backend offers APIs for authentication, course management, and other functions. MongoDB enables flexible data storage. The architecture aims to provide a scalable, responsive platform for online learning.
A Comprehensive Review of Integrating IoT and Blockchain Technologies in the ...IRJET Journal
This paper proposes integrating Internet of Things (IoT) and blockchain technologies to help implement objectives of India's National Education Policy (NEP) in the education sector. The paper discusses how blockchain could be used for secure student data management, credential verification, and decentralized learning platforms. IoT devices could create smart classrooms, automate attendance tracking, and enable real-time monitoring. Blockchain would ensure integrity of exam processes and resource allocation, while smart contracts automate agreements. The paper argues this integration has potential to revolutionize education by making it more secure, transparent and efficient, in alignment with NEP goals. However, challenges like infrastructure needs, data privacy, and collaborative efforts are also discussed.
A REVIEW ON THE PERFORMANCE OF COCONUT FIBRE REINFORCED CONCRETE.IRJET Journal
This document provides a review of research on the performance of coconut fibre reinforced concrete. It summarizes several studies that tested different volume fractions and lengths of coconut fibres in concrete mixtures with varying compressive strengths. The studies found that coconut fibre improved properties like tensile strength, toughness, crack resistance, and spalling resistance compared to plain concrete. Volume fractions of 2-5% and fibre lengths of 20-50mm produced the best results. The document concludes that using a 4-5% volume fraction of coconut fibres 30-40mm in length with M30-M60 grade concrete would provide benefits based on previous research.
Optimizing Business Management Process Workflows: The Dynamic Influence of Mi...IRJET Journal
The document discusses optimizing business management processes through automation using Microsoft Power Automate and artificial intelligence. It provides an overview of Power Automate's key components and features for automating workflows across various apps and services. The document then presents several scenarios applying automation solutions to common business processes like data entry, monitoring, HR, finance, customer support, and more. It estimates the potential time and cost savings from implementing automation for each scenario. Finally, the conclusion emphasizes the transformative impact of AI and automation tools on business processes and the need for ongoing optimization.
Multistoried and Multi Bay Steel Building Frame by using Seismic DesignIRJET Journal
The document describes the seismic design of a G+5 steel building frame located in Roorkee, India according to Indian codes IS 1893-2002 and IS 800. The frame was analyzed using the equivalent static load method and response spectrum method, and its response in terms of displacements and shear forces were compared. Based on the analysis, the frame was designed as a seismic-resistant steel structure according to IS 800:2007. The software STAAD Pro was used for the analysis and design.
Cost Optimization of Construction Using Plastic Waste as a Sustainable Constr...IRJET Journal
This research paper explores using plastic waste as a sustainable and cost-effective construction material. The study focuses on manufacturing pavers and bricks using recycled plastic and partially replacing concrete with plastic alternatives. Initial results found that pavers and bricks made from recycled plastic demonstrate comparable strength and durability to traditional materials while providing environmental and cost benefits. Additionally, preliminary research indicates incorporating plastic waste as a partial concrete replacement significantly reduces construction costs without compromising structural integrity. The outcomes suggest adopting plastic waste in construction can address plastic pollution while optimizing costs, promoting more sustainable building practices.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Batteries -Introduction – Types of Batteries – discharging and charging of battery - characteristics of battery –battery rating- various tests on battery- – Primary battery: silver button cell- Secondary battery :Ni-Cd battery-modern battery: lithium ion battery-maintenance of batteries-choices of batteries for electric vehicle applications.
Fuel Cells: Introduction- importance and classification of fuel cells - description, principle, components, applications of fuel cells: H2-O2 fuel cell, alkaline fuel cell, molten carbonate fuel cell and direct methanol fuel cells.
artificial intelligence and data science contents.pptxGauravCar
What is artificial intelligence? Artificial intelligence is the ability of a computer or computer-controlled robot to perform tasks that are commonly associated with the intellectual processes characteristic of humans, such as the ability to reason.
› ...
Artificial intelligence (AI) | Definitio
Software Engineering and Project Management - Introduction, Modeling Concepts...Prakhyath Rai
Introduction, Modeling Concepts and Class Modeling: What is Object orientation? What is OO development? OO Themes; Evidence for usefulness of OO development; OO modeling history. Modeling
as Design technique: Modeling, abstraction, The Three models. Class Modeling: Object and Class Concept, Link and associations concepts, Generalization and Inheritance, A sample class model, Navigation of class models, and UML diagrams
Building the Analysis Models: Requirement Analysis, Analysis Model Approaches, Data modeling Concepts, Object Oriented Analysis, Scenario-Based Modeling, Flow-Oriented Modeling, class Based Modeling, Creating a Behavioral Model.
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...shadow0702a
This document serves as a comprehensive step-by-step guide on how to effectively use PyCharm for remote debugging of the Windows Subsystem for Linux (WSL) on a local Windows machine. It meticulously outlines several critical steps in the process, starting with the crucial task of enabling permissions, followed by the installation and configuration of WSL.
The guide then proceeds to explain how to set up the SSH service within the WSL environment, an integral part of the process. Alongside this, it also provides detailed instructions on how to modify the inbound rules of the Windows firewall to facilitate the process, ensuring that there are no connectivity issues that could potentially hinder the debugging process.
The document further emphasizes on the importance of checking the connection between the Windows and WSL environments, providing instructions on how to ensure that the connection is optimal and ready for remote debugging.
It also offers an in-depth guide on how to configure the WSL interpreter and files within the PyCharm environment. This is essential for ensuring that the debugging process is set up correctly and that the program can be run effectively within the WSL terminal.
Additionally, the document provides guidance on how to set up breakpoints for debugging, a fundamental aspect of the debugging process which allows the developer to stop the execution of their code at certain points and inspect their program at those stages.
Finally, the document concludes by providing a link to a reference blog. This blog offers additional information and guidance on configuring the remote Python interpreter in PyCharm, providing the reader with a well-rounded understanding of the process.