The process of detecting the social distance by using machine learning which initially explores the object detection, tracking and distance calculation between the detected objects. You can trust or team as they have the required machine learning knowledge to deliver a high quality of research work. Various areas of social distance detection are covered under machine learning and we have completed many projects successfully. We approach such kind of project that extracts the benefits and seeking the attention during the COVID-19 pandemic situation. It maintains the distance between individuals that became an essential precaution measures,
For implementing a social detection system using machine leading, then follow the essential guide lines,
We construct a machine learning model for identifying the individuals in a given particular area if they are maintaining a safe social distance. This is the primary goal of this project.
The well-executed or implemented social distance detection system is developed by us with the help of this article and it must act as an efficient tool in the field of public health measures. It helps monitor and apply social distancing protocols in various applications. Still always deal with knowledge in noble considerations, specifically care about privacy. Research Manuscript will be done tactfully as we have many qualified, expert thesis writers to deliver the work in high quality as per your interest.
Trust our team for to deliver your social distance detection thesis writing. One to one service will be offered with an experienced thesis team for your machine learning projects. Here we will research and carry out your thesis writing from scratch so we assure the ideas that they bring is original.
Social distancing, Gaussian lens model, machine learning
The aim of our paper is to improve a social distancing detector over two comparison models: a model executing the Gaussian thin lens formula and a machine learning method. We can test the both models for accuracy to detect distance from a first person webcam. The result shows that the machine learning model gives the best accuracy rate. ML method can be able to detect distance from webcam.
YOLO, CNN, Social distancing, COVID-19, Object Detection, Computer Vision, Deep Learning
Our paper monitors social distance in public places by using an automated system. We detect walkers in video frames caught by camera and utilize a YOLOv3 object detection method. To calculate the distance between everyone in a 2D plane by change the frames into top-down view point. When any of them does not maintain a distance among themselves then our system will alert them with a red frame and a red line.
You Only Look Once Algorithm, CNN techniques, R-CNN, Neural Networks
By using CNN method to be compared with YOLO to detect the social distancing is the aim of our paper. We used two DL based methods like CNN and YOLO to detect social distance. For image recognition and classification, we used CNN a DL method where NNs can be utilized here. To detect an image, we have used a Google AI open image dataset. Our YOLO method gives the best result for social distance detection.
Physical Distancing Rule Violation Detection, PP-Yolo, Tensorflow, CORONA Virus, Artificial Neural Network
We used PP-YOLO (PaddlePaddle – You only look once) and tensor flow to detect the physical distancing violation. Tensor flow pattern recognition or object detection tool when walkers can detected inevitably and PP-YOLO classifies the distance between the persons or whether they follow the distance or not. Our proposed system detects the rule violation and gets a increased accuracy rate.
Pandemic, MobileNet SSD, Person detection, Distance estimation, Surveillance system, Alert signal
Our paper suggests a method MobileNet SSD that can automatically evaluate the interactive distance among people. Our suggested method first detects the person in a video stream and that will symbolize people according to their distance by utilizing various colour indicators. We offer an alert signal to make the people attentive. Our suggested system also provides an observation system to alert the government in any harmful situation.
Euclidean Distance, Pedestrian detection, Region of Interest, Coloured Boundary boxes.
To decrease the covid-19 virus spread we have to utilize the DL method to monitor the distance between the walkers. To make everyone aware we have to use the discovery tool by examining a real time video. YOLO V3 method can be utilized to pre-train the object detection that has given the video frames from camera as input and can be used for social distance detection. After that the video frame can be converted to hierarchical 2D view. Our method uses a live footage to allow the proper execution.
Yolov5, Facemask detection, social distance detection, yolov4, Confidence Score, mAP.
YOLOv5 and a pre-trained framework are the methods used for complex mask detection. Our suggested method aims to accomplish a structure to detect social distance based on YOLOv5 architecture to control, monitor, accomplish and reduce the communication among people. According to pixel information and violation threshold the euclidean distance among people can be spotted in video.
Our goal is to decrease the disease transfer by respiratory droplets such as covid-19 and we suggest an automated approach to detect social distance and face mask. We used two cascaded YOLO the first one detect human environment and compute distance among them and the second one detect human face with or without mask. At last our method uses red encircled face who didn’t obey the rules. We uses two feature extraction methods such as transfer learning method and the feature specific task using LBP layer and classification layer.
Our paper suggests that an online multi-layer social distance detection system is to detect the distance between people and then classify the distance and our system stream video of fixed camera and then to detect the people by utilizing multi layers. At first we uses the YOLO-4 method with CNN and surround it by rectangle and the second is to analyse the detected person and last we can estimate the distance among them is accepted or not.
Temperature analysis, Jetson nano, Distributed surveillance system
Our paper uses an AI based social distance classification of persons by utilizing thermal images. YOLOv2 is a DL method that can be established to detect and track people in both indoor and outdoor. We can attain the images through thermal camera and create an AI system for tracking, social distance classification and body temperature monitoring. Our suggested method can be executed on a distributed surveillance video system to watch people from several cameras in one unified monitor system.
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