SPOTTER: Detection of Human Beings Under Collapsed Environment

Collapse of man-made structures, such as buildings and bridges earth quakes and fire accident, occur with varying frequency across the world. In such a scenario, the survived human beings are likely to get trapped in the cavities created by collapsed building material. During post disaster rescue operations, searchand-rescue crews have a very limited or no knowledge of the presence, location, and number of the trapped victims. Deep learning is a fast-growing domain of machine learning, mainly for solving problems in computer vision. One of the implementation of deep learning is detection of objects including humans, based on video stream. Thus, the presence of a human buried under earthquake rubble or hidden behind barriers can be identified using deep learning. This is done with the help of USB camera which can be inserted into the rubble. Spotter also gives an audio message about the location of the human presence and gives the area where the human is likely to be present. Human detection is done with the help of Computer Vision using OpenCV.

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Traffic Sign Classification Using Convolutional Neural Networks and Computer Vision

Turkish Journal of Computer and Mathematics Education (TURCOMAT) ◽

10.17762/turcomat.v12i3.1715 ◽

2021 ◽

Vol 12 (3) ◽

pp. 4244-4250

Author(s):

Anuraag Velamati Et.al

Keyword(s):

Neural Network ◽

Machine Learning ◽

Neural Networks ◽

Computer Vision ◽

Deep Learning ◽

Image Classification ◽

Human Beings ◽

Traffic Sign ◽

The World ◽

Traffic Sign Classification

The world is quickly and continuously advancing towards better technological advancements that will make life quite easier for us, human beings [22]. Humans are looking for more interactive and advanced ways to improve their learning. One such dream is making a machine think like a computer, which lead to innovations like AI and deep learning [25]. The world is running at a higher pace in the domain of AI, deep learning, robotics and machine learning Using this knowledge and technology, we could develop anything right now [36]. As a part of sub-domain, the introduction of Convolution Neural Networks made deep learning extensively strong in the domain of image classification and detection [1]..The research that we have conducted is one of its kind. Our research used Convolution Neural Network, TensorFlow and Keras.

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Deep Learning-Enabled Semantic Inference of Individual Building Damage Magnitude from Satellite Images

Algorithms ◽

10.3390/a13080195 ◽

2020 ◽

Vol 13 (8) ◽

pp. 195 ◽

Cited By ~ 2

Author(s):

Bradley J. Wheeler ◽

Hassan A. Karimi

Keyword(s):

Computer Vision ◽

Deep Learning ◽

Natural Disasters ◽

Satellite Images ◽

Management Decision ◽

Semantic Inference ◽

The World ◽

Major Bottleneck ◽

Post Disaster ◽

Management Decision Support

Natural disasters are phenomena that can occur in any part of the world. They can cause massive amounts of destruction and leave entire cities in great need of assistance. The ability to quickly and accurately deliver aid to impacted areas is crucial toward not only saving time and money, but, most importantly, lives. We present a deep learning-based computer vision model to semantically infer the magnitude of damage to individual buildings after natural disasters using pre- and post-disaster satellite images. This model helps alleviate a major bottleneck in disaster management decision support by automating the analysis of the magnitude of damage to buildings post-disaster. In this paper, we will show our methods and results for how we were able to obtain a better performance than existing models, especially in moderate to significant magnitudes of damage, along with ablation studies to show our methods and results for the importance and impact of different training parameters in deep learning for satellite imagery. We were able to obtain an overall F1 score of 0.868 with our methods.

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Marine Vision-Based Situational Awareness Using Discriminative Deep Learning: A Survey

Journal of Marine Science and Engineering ◽

10.3390/jmse9040397 ◽

2021 ◽

Vol 9 (4) ◽

pp. 397

Author(s):

Dalei Qiao ◽

Guangzhong Liu ◽

Taizhi Lv ◽

Wei Li ◽

Juan Zhang

Keyword(s):

Computer Vision ◽

Deep Learning ◽

Homeland Security ◽

Situational Awareness ◽

Future Research ◽

Target Vessel ◽

Human Beings ◽

Multimodal Data ◽

Existing Problems ◽

Multimodal Data Fusion

The primary task of marine surveillance is to construct a perfect marine situational awareness (MSA) system that serves to safeguard national maritime rights and interests and to maintain blue homeland security. Progress in maritime wireless communication, developments in artificial intelligence, and automation of marine turbines together imply that intelligent shipping is inevitable in future global shipping. Computer vision-based situational awareness provides visual semantic information to human beings that approximates eyesight, which makes it likely to be widely used in the field of intelligent marine transportation. We describe how we combined the visual perception tasks required for marine surveillance with those required for intelligent ship navigation to form a marine computer vision-based situational awareness complex and investigated the key technologies they have in common. Deep learning was a prerequisite activity. We summarize the progress made in four aspects of current research: full scene parsing of an image, target vessel re-identification, target vessel tracking, and multimodal data fusion with data from visual sensors. The paper gives a summary of research to date to provide background for this work and presents brief analyses of existing problems, outlines some state-of-the-art approaches, reviews available mainstream datasets, and indicates the likely direction of future research and development. As far as we know, this paper is the first review of research into the use of deep learning in situational awareness of the ocean surface. It provides a firm foundation for further investigation by researchers in related fields.

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Research on Sports Training Action Recognition Based on Deep Learning

Scientific Programming ◽

10.1155/2021/3396878 ◽

2021 ◽

Vol 2021 ◽

pp. 1-8

Author(s):

Peng Wang

Keyword(s):

Computer Vision ◽

Deep Learning ◽

Learning Algorithm ◽

Rapid Development ◽

Human Motion ◽

Medical Monitoring ◽

Human Beings ◽

Sports Training ◽

Motion Recognition ◽

Human Motion Recognition

With the rapid development of science and technology in today’s society, various industries are pursuing information digitization and intelligence, and pattern recognition and computer vision are also constantly carrying out technological innovation. Computer vision is to let computers, cameras, and other machines receive information like human beings, analyze and process their semantic information, and make coping strategies. As an important research direction in the field of computer vision, human motion recognition has new solutions with the gradual rise of deep learning. Human motion recognition technology has a high market value, and it has broad application prospects in the fields of intelligent monitoring, motion analysis, human-computer interaction, and medical monitoring. This paper mainly studies the recognition of sports training action based on deep learning algorithm. Experimental work has been carried out in order to show the validity of the proposed research.

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Face Mask Detection using Deep Learning to Manage Pandemic Guidelines

10.54060/jmss/001.02.003 ◽

2021 ◽

Vol 1 (2) ◽

pp. 1-21

Author(s):

Ankita Singh ◽

Keyword(s):

Computer Vision ◽

Deep Learning ◽

Super Resolution ◽

Face Mask ◽

Image Sequence ◽

Web Security ◽

Security Systems ◽

Detection Of Objects ◽

Image Super Resolution ◽

The Web

The field of Computer Vision is a branch of science of the computers and systems of software in which one can visualize and as well as comprehend the images and scenes given in the input. This field is consisting of numerous aspects for example image recognition, the detection of objects, generation of images, image super resolution and more others. Object detection is broadly utilized for the detection of faces, the detection of vehicles, counting of pedestrians on a certain street, images displayed on the web, security systems and cars with the feature of self-driving. This process also encompasses the precision of every technique for recognizing the objects. The detection of objects is a crucial task; however, it is also a very challenging vision task. It is an analytical subdivide of various applications such as searching of images, image auto-annotation or scene understanding and tracking of various objects. The tracking of objects in motion of video image sequence was one of the most important subjects in computer vision.

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Facemask Detection With an Alarm System and Email Notification Using Deep Learning to Prevent Spread of COVID-19

10.21203/rs.3.rs-658656/v1 ◽

2021 ◽

Author(s):

Vinita Sankla ◽

Savitanandan Patidar ◽

Vishal kushwaha ◽

Ashish Bagwari ◽

Rahul Tiwari ◽

...

Keyword(s):

Computer Vision ◽

Deep Learning ◽

Economic Impact ◽

Real Time ◽

Human Life ◽

Face Mask ◽

Video Stream ◽

Alarm System ◽

The World ◽

Socio Economic Impact

Abstract COVID-19 is one of the most dangerous viruses which caused a pandemic in human life, not only in terms of direct casualties but also regarding socio-economic impact. The outbreak quickly spread around the world. The 1st anniversary of the global Corona virus pandemic gets passed away in 2021, but still, no way to tell how long the pandemic will continue. After analyzing a report by WHO of covid-19, to minimize the rate of covid-19 transmission, our national government advised citizens to wear face masks. A model using deep learning and MobileNetV2 for face mask detection is presented. This method was trained and checked on the real-time dataset. There are 3,833 images in the Medical Masks Dataset, including 1918 images of people wearing no mask and 1915 images of people wearing masks. We adopted OpenCV to detect faces in real-time from a live stream captured with our webcam. With the aid of computer vision and deep learning, we hope to classify whether or not the person in the video stream is wearing a face mask. If the camera captures a face without a mask an Email notification will be sent out to the administrator and the system alarm will ring.

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Face Mask Detection Using Deep Learning and Computer Vision

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2022.39880 ◽

2022 ◽

Vol 10 (1) ◽

pp. 511-516

Author(s):

Prod. Roshan R. Kolte

Keyword(s):

Computer Vision ◽

Deep Learning ◽

Virus Transmission ◽

Face Mask ◽

Video Stream ◽

World Health ◽

Health Crisis ◽

Protection Method ◽

The World ◽

Health Organization

Abstract: COVID-19 pandemic has rapidly affected our day-to-day life the world trade and movements. Wearing a face mask is very essentials for protecting against virus. People also wear mask to cover themselves in order to reduce the spread of covid virus. The corona virus covid-19 pandemic is causing a global health crisis so the effective protection method is wearing a face mask in public area according to the world health organization (WHO). The covid-19 pandemic forced government across the world to impose lockdowns to prevent virus transmission report indicates that wearing face mask while at work clearly reduce the risk of transmission .we will use the dataset to build a covid-19 face mask detector with computer vision using python,opencv,tensorflow,keras library and deep learning. Our goal is to identify whether the person on image or live video stream is wearing mask or not wearing face mask this can help to society and whole organization to avoid the transfer of virus one person to antother.we used computer vision and deep learning modules to detect a with mask image and without mask image. Keywords: face detection, face recognition, CNN, SVM, opencv, python, tensorflow, keras.

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ON METHODS OF OBJECT DETECTION IN VIDEO STREAMS

Computer systems and network ◽

10.23939/csn2020.01.080 ◽

2017 ◽

Vol 2 (1) ◽

pp. 80-87

Author(s):

Puyda V. ◽

◽

Stoian. A.

Keyword(s):

Computer Vision ◽

Object Detection ◽

Open Source ◽

Feature Detection ◽

Video Stream ◽

Object Identification ◽

Vision Systems ◽

Modern Computer ◽

Computer Vision Systems ◽

Open Source Hardware

Detecting objects in a video stream is a typical problem in modern computer vision systems that are used in multiple areas. Object detection can be done on both static images and on frames of a video stream. Essentially, object detection means finding color and intensity non-uniformities which can be treated as physical objects. Beside that, the operations of finding coordinates, size and other characteristics of these non-uniformities that can be used to solve other computer vision related problems like object identification can be executed. In this paper, we study three algorithms which can be used to detect objects of different nature and are based on different approaches: detection of color non-uniformities, frame difference and feature detection. As the input data, we use a video stream which is obtained from a video camera or from an mp4 video file. Simulations and testing of the algoritms were done on a universal computer based on an open-source hardware, built on the Broadcom BCM2711, quad-core Cortex-A72 (ARM v8) 64-bit SoC processor with frequency 1,5GHz. The software was created in Visual Studio 2019 using OpenCV 4 on Windows 10 and on a universal computer operated under Linux (Raspbian Buster OS) for an open-source hardware. In the paper, the methods under consideration are compared. The results of the paper can be used in research and development of modern computer vision systems used for different purposes. Keywords: object detection, feature points, keypoints, ORB detector, computer vision, motion detection, HSV model color

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