scholarly journals Social Distancing Detector using YOLO v3 Image Processing Algorithm

2021 ◽  
pp. 783-787
Author(s):  
Ms. Kavita S. Kumavat ◽  
Aman Kumar Sao ◽  
Harish Khedekar ◽  
Chirag Panpaliya ◽  
Shantanu Korde
Keyword(s):  
Image Processing ◽  
Public Awareness ◽  
Image Processing Algorithm ◽  
Social Distancing ◽  
Detection Model ◽  
Model Object ◽  
Learning Framework ◽  
The Social ◽  
Feasible Option ◽  
Bounding Boxes

The lack of public awareness and negligence, the pandemic due to coronavirus(covid19) has brought a global crisis with its deadly spread to more than 180 countries, and about 147 million confirmed cases along with 3.11 million deaths globally as of 26th April 2021. Due to the absence of the vaccine against the covid19 the world has suffered a lot. Though scientists have developed several vaccines then also the pandemic is still out of control so therefore the only feasible option available to us is social distancing. And this notion motivated us to bring up the idea of a social distancing detector using image processing which includes a deep learning framework for automation task monitoring. The framework utilizes the YOLO v3 model object detection model to separate moving people from the background and to detect people by using bounding boxes. The basic idea of this article is to analyze the social distancing violation index rate that how many people violate the rule of social distancing in a particular interval of time.

scholarly journals Social Distancing Detector using Image Processing

2021 ◽  
pp. 542-545
Author(s):  
Aman Kumar Sao ◽  
Harish Khedekar ◽  
Chirag Panpaliya ◽  
Shantanu Korde ◽  
Ms. Kavita S. Kumavat
Keyword(s):  
Image Processing ◽  
Public Awareness ◽  
Social Distancing ◽  
Detection Model ◽  
Model Object ◽  
Learning Framework ◽  
The Social ◽  
The World ◽  
Feasible Option ◽  
Bounding Boxes

The lack of public awareness and negligence, the pandemic due to coronavirus(covid19) has brought a global crisis with its deadly spread to more than 180 countries, and about 147 million confirmed cases along with 3.11 million deaths globally as of 26th April 2021. Due to the absence of the vaccine against the covid19 the world has suffered a lot. Though scientists have developed several vaccines then also the pandemic is still out of control so therefore the only feasible option available to us is social distancing. And this notion motivated us to bring up the idea of a social distancing detector using image processing which includes a deep learning framework for automation task monitoring. The framework utilizes the YOLO v3 model object detection model to separate moving people from the background and to detect people by using bounding boxes. The basic idea of this article is to analyze the social distancing violation index rate that how many people violate the rule of social distancing in a particular interval of time.

scholarly journals Analysis and Implementation of Fruit/Leave Disease Detection using Image Processing and Neural Approach

2021 ◽  
Vol 7 (09) ◽  
pp. 186-194
Author(s):  
Krishna Madheshiya, Prashant Richhariya and Dr. Anita Soni
Keyword(s):  
Image Processing ◽  
Plant Disease ◽  
Plant Diseases ◽  
Disease Detection ◽  
New Approach ◽  
Detection Model ◽  
Learning Framework ◽  
Deep Cnn ◽  
First Time ◽  
Leaf Image

The latest generation of convolution neural networks (CNNs) has achieved impressive results in the field of image classification. This paper is concerned with a new approach to the development of fruit/plant disease detection model, based on leaf image processing and classification, by the use of ANN. Novel way of training and the methodology used facilitate a quick and easy system implementation in practice. The developed model is able to recognize 13 different types of plant diseases out of healthy leaves, with the ability to distinguish plant leaves from their surroundings. According to our knowledge, this method for plant disease recognition has been proposed for the first time. All essential steps required for implementing this disease recognition model are fully described throughout the paper, starting from gathering images in order to create a database, assessed by agricultural experts. Caffe, a deep learning framework developed by Berkley Vision and Learning Centre, was used to perform the deep CNN training

scholarly journals Social Distance Monitoring Using Drone

2021 ◽  
Vol 10 (3) ◽  
pp. 2183-2187
Keyword(s):  
Social Distance ◽  
Task Force ◽  
Physical Distance ◽  
Video Sequences ◽  
Distance Threshold ◽  
Social Distancing ◽  
Detection Model ◽  
Bounding Box ◽  
The Social ◽  
Information Set

Within the present times, the priority and danger of the COVID-19 virus still stand large. Manual looking of social distancing norms is impractical with associate in nursing oversized population moving concerning and with the short task force and resources to administer them. There is a want for a light-weight, robust, and 24*7 video-monitoring system that automates this technique. This paper proposes a comprehensive and effective resolution to perform person detection and social distancing violation detection exploitation object detection, agglomeration and Convolution Neural Network (CNN) based binary classifier. The framework uses the Scaled-YOLOv4 seeing paradigm to identify humans in video sequences. The detection formula uses a pre-trained formula that's connected to an additional trained layer using a frontal human information set. The detection model identifies peoples exploitation detected bounding box information. Exploitation the mathematician distance, the detected bounding box centroid’s pairwise distances of individuals unit determined. A threshold is used to estimate the social distance violations between people. Here, we have a tendency to tend to use Associate in nursing approximation of physical distance to pixel to line the sting price. A violation threshold is established to live whether or not the gap price breaches the minimum social distance threshold. To boot, a trailing formula is employed to sight individuals in video sequences such the one that violates/ crosses the social distance threshold is, additionally, being half-track. Experiments unit administered on whole completely different video sequences to ascertain the efficiency of the model. Findings indicate that the developed framework successfully distinguishes individuals UN agency walk too about to and breach/violate social distances.

Estimating OLED Display Device Lifetime from pixel and screen brightness and its application

2019 ◽  
Vol 2019 (1) ◽  
pp. 331-338 ◽  
Author(s):  
Jérémie Gerhardt ◽  
Michael E. Miller ◽  
Hyunjin Yoo ◽  
Tara Akhavan
Keyword(s):  
Image Processing ◽  
Power Consumption ◽  
Model Parameters ◽  
Display Device ◽  
Image Processing Algorithm ◽  
Pixel Value ◽  
Device Lifetime ◽  
The Impact ◽  
Oled Display

In this paper we discuss a model to estimate the power consumption and lifetime (LT) of an OLED display based on its pixel value and the brightness setting of the screen (scbr). This model is used to illustrate the effect of OLED aging on display color characteristics. Model parameters are based on power consumption measurement of a given display for a number of pixel and scbr combinations. OLED LT is often given for the most stressful display operating situation, i.e. white image at maximum scbr, but having the ability to predict the LT for other configurations can be meaningful to estimate the impact and quality of new image processing algorithms. After explaining our model we present a use case to illustrate how we use it to evaluate the impact of an image processing algorithm for brightness adaptation.

scholarly journals Tourist Route Optimization in the Context of Covid-19 Pandemic

2021 ◽  
Vol 13 (10) ◽  
pp. 5492
Author(s):  
Cristina Maria Păcurar ◽  
Ruxandra-Gabriela Albu ◽  
Victor Dan Păcurar
Keyword(s):  
Travelling Salesman Problem ◽  
Route Planning ◽  
Route Optimization ◽  
Travelling Salesman ◽  
Social Distancing ◽  
Tourist Destinations ◽  
Innovative Method ◽  
The Social ◽  
Immediate Response

The paper presents an innovative method for tourist route planning inside a destination. The necessity of reorganizing the tourist routes within a destination comes as an immediate response to the Covid-19 crisis. The implementation of the method inside tourist destinations can bring an important advantage in transforming a destination into a safer one in times of Covid-19 and post-Covid-19. The existing trend of shortening the tourist stay length has been accelerated while the epidemic became a pandemic. Moreover, the wariness for future pandemics has brought into spotlight the issue of overcrowded attractions inside a destination at certain moments. The method presented in this paper proposes a backtracking algorithm, more precisely an adaptation of the travelling salesman problem. The method presented is aimed to facilitate the navigation inside a destination and to revive certain less-visited sightseeing spots inside a destination while facilitating conformation with the social distancing measures imposed for Covid-19 control.

scholarly journals Robust estimation of SARS-CoV-2 epidemic in US counties

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hanmo Li ◽  
Mengyang Gu
Keyword(s):  
Transmission Rate ◽  
Public Awareness ◽  
Mitigation Measures ◽  
Infectious Period ◽  
Protective Measures ◽  
Social Distancing ◽  
The Public ◽  
The Us ◽  
Local Officials ◽  
Us Counties

AbstractThe COVID-19 outbreak is asynchronous in US counties. Mitigating the COVID-19 transmission requires not only the state and federal level order of protective measures such as social distancing and testing, but also public awareness of time-dependent risk and reactions at county and community levels. We propose a robust approach to estimate the heterogeneous progression of SARS-CoV-2 at all US counties having no less than 2 COVID-19 associated deaths, and we use the daily probability of contracting (PoC) SARS-CoV-2 for a susceptible individual to quantify the risk of SARS-CoV-2 transmission in a community. We found that shortening by $$5\%$$ 5 % of the infectious period of SARS-CoV-2 can reduce around $$39\%$$ 39 % (or 78 K, $$95\%$$ 95 % CI: [66 K , 89 K ]) of the COVID-19 associated deaths in the US as of 20 September 2020. Our findings also indicate that reducing infection and deaths by a shortened infectious period is more pronounced for areas with the effective reproduction number close to 1, suggesting that testing should be used along with other mitigation measures, such as social distancing and facial mask-wearing, to reduce the transmission rate. Our deliverable includes a dynamic county-level map for local officials to determine optimal policy responses and for the public to better understand the risk of contracting SARS-CoV-2 on each day.

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