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,

  1. Objective Definition

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.

  1. Data Collection
  • Available public datasets are utilized by us for detecting people or fetching of custom footage. Ensure that it must contain required permissions while collecting our own data.
  • COCO and Pascal VOC datasets are best initial points for object detection.
  1. Data Pre-processing
  • Annotate data: The individuals in the image are examined while assuming some of our custom datasets.
  • Data Augmentation: For developing generalization, increase the data with rotations, flips and cropping.
  1. People Detection
  • Pre-trained Models: We deploy object detection architectures such as, SSD, YOLO, or Faster R-CNN pre-trained on datasets like COCO for detecting people.
  • Fine-tuning: When we are working in a particular dataset or under specific conditions, then establish with fine-tuning the model.
  1. Distance Calculation
  • The detected bounding boxes are being utilized for calculating the distance between individuals.
  • For measuring the absolute distance, we must outlook the model deeply. The simple and clear approach includes balancing our system by using objects of familiar size in the frame.
  1. Visualization
  • We highlight the individuals in green colour who are at the safe distance and mark the individuals in red who are not safe in distance.
  • Another choice is, draw lines between the people who are close enough, highlight the distance in colour.
  1. Real-time Implementation
  • If our objective is real-time detection then look at lightweight models like YOLOv3 -tiny or Mobile Net.
  • Advanced libraries like Tensorflow, Lite or NVIDIA TensorRT are employed for enhanced inference speeds.
  1. Evaluation
  • Accuracy: How accurately does the model detect individuals?
  • Speed: Does the model run in real-time, especially if deployed on surveillance systems?
  • False Positives/Negatives: When our system inaccurately accuses individuals then the cases are being followed as close enough too or misses infraction.
  1. Optimization
  • Reducing False Alarms: This includes tracking an algorithm such as SORT or DeepSORT for keeping a track over the frames, minimize the chances of momentary false alarms.
  • Post-processing: The techniques are executed by us like non-maximum suppression for decreases the overlapping detections in our system.
  1. Deployment
  • Our system is applicable on surveillance cameras in public places, offices and factories.
  • A dashboard is developed for figuring the real-time breaches and offers data analysis. For example, in what manner the breaches occur, the times of the day accommodated with usual breaches).
  1. Feedback and Continuous Learning
  • Collect the reviews about the performance of the model and modify with required adjustments.
  • The model must regularly get updated by us with new data or information for maintaining accuracy.
  1. Conclusion and Future Work
  • We must note the performance of the system, challenging the obstacles and the capable efficient improvements.
  • The future enhancements includes,
  • Integrating face mask detection
  • Incorporating audible alarms for breaches
  • Heat maps for point out the zones with high breach.

Suggestions:

  • Privacy Concerns: For checking an individual identities which are not detectable and label the privacy problems, then use low-resolution data or mask detected faces.
  • Calibration: The accurate distance is calculated by us through camera calibration, particularly if cameras are at various angles.

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.

Social Distance Detection using Machine Learning Projects

Social Distance Detection using Machine Learning Thesis Ideas

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.

  1. Smart Detection of Social Distance Violations using Gaussian Lens Model and Deep Learning

Keywords:

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.   

  1. Social Distancing Detection AI System using YOLO

Keywords:

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.

  1. Comparative Analysis of Novel Social Distancing Detector using Enhanced You Only Look Once and CNN Deep Learning Approach with improved Accuracy

Keywords:

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. 

  1. COVID-19: Automatic Social Distancing Rule Voilation Detection using PP-Yolo & Tensorflow in OpenCV

 Keywords:

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.

  1. A Neural Network based Social Distance Detection

Keywords:

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.

  1. Social Distance Detection and Alert System using Deep Learning and Computer Vision

Keywords:

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.

  1. Effective Face Mask And Social Distance Detection with Alert System for Covid-19 Using YOLOv5 Model

Keywords:

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.

  1. A hybrid LBP-DCNN based feature extraction method in YOLO: An application form asked face and social distance detection

Keywords:

Human detection

            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.

  1. Online Multi-Layers Social Distance Detection in Iraqi Schools

Keywords: 

            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.  

  1. Implementing a real-time, AI-based, people detection and social distancing measuring system for Covid-19

Keywords:

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.

Important Research Topics