Social Distancing Detection Using AI: Project Outline
The COVID-19 is an unparalleled crisis leading to huge number of casualties and security problems. In order to reduce the spread of coronavirus, people often wear masks to protect themselves and then maintain the social distance. A primary focus of researchers during the on-going coronavirus pandemic is to come up with suggestions to handle this problem through rapid and efficient solutions.
Social distancing is an important step to avoid spreading COVID-19 virus which involves increasing the physical space between people. Implementing social distancing during the ongoing global pandemic is an important step. Maintaining a particular distance from other people lessens your chances of contracting COVID-19.
Social Distancing Detection is a system used to detect whether people are following social distancing guidelines in public areas. The system uses cameras and computer vision algorithms to detect whether people are following the appropriate distance from one another and if they are not, it can alert the authorities. The system can also be used to measure the effectiveness of different social distancing policies in different areas.
The proposed work includes usage of yolo v3 which is a fully convolutional neural network algorithm to detect the people in the video frames. OpenCV which is a real-time computer vision library is used to feed the input framed from the images or videos captured by the cameras to the yolo v3 neural network. The system will then provide the results for the particular region and provide a statistical analysis of the region captured by the cameras. Yolo v3 which is capable of recognizing 80 different objects in given input images
Applications
- behavior and ensure that people are maintaining a safe distance from each other while shopping.
- Public transportation: Public transportation systems can use social distancing detection to monitor passenger behavior and ensure that people are maintaining a safe distance from each other while waiting for or using public transportation.
- Airports: Airports can use social distancing detection to monitor passenger behavior and ensure that people are maintaining a safe distance from each other while waiting in lines, going through security, and boarding flights.
- Schools: Schools can use social distancing detection to monitor student behavior and ensure that students are maintaining a safe distance from each other while in classrooms, hallways, and other common areas.
- Offices: Offices can use social distancing detection to monitor employee behavior and ensure that employees are maintaining a safe distance from each other while in the office.
- Public spaces: Public spaces such as parks, beaches, and outdoor markets can use social distancing detection to monitor visitor behavior and ensure that people are maintaining a safe distance from each other while enjoying public spaces.
Requirements
Hardware requirement
A laptop with:
- A CPU with clock speed of atleast 2.5GHz.
- GPU(atleast 4GB VRAM).
- RAM (more than 8GB ).
- SSD (more than 256GB)
Software requirement
- Python: Python is a popular programming language for machine learning and computer vision tasks.
- Computer vision libraries: Computer vision libraries such as OpenCV, TensorFlow, and PyTorch can be used to analyze video feeds from cameras and identify people in the frame.
- Object detection algorithms: Object detection algorithms such as YOLO (You Only Look Once) and Faster R-CNN can be used to detect people in the video feed.
- Distance measuring algorithms: Algorithms such as stereo vision and depth estimation can be used to measure the distance between people in the video feed.
- Machine learning models: Machine learning models such as support vector machines (SVM) and deep neural networks (DNN) can be used to classify people in the video feed as either maintaining a safe distance or not.
- Cloud computing services: Cloud computing services such as Amazon Web Services (AWS) and Google Cloud Platform (GCP) can be used to process video feeds from cameras and run machine learning models in real-time.
Tool:
IDLE is an integrated development environment for Python, which has been bundled with the default implementation of the language since 1.5.2b1. It is packaged as an optional part of the Python packaging with many Linux distributions. It is completely written in Python and the Tkinter GUI toolkit.
Technology:
Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term may also be applied to any machine that exhibits traits associated with a human mind such as learning and problem-solving.
What you’ll Learn after doing this project?
- Working with computer vision algorithms and image processing techniques.
- Implementing deep learning algorithms for object detection and recognition.
- Understanding and using machine learning models
- Working with real-time video data and creating an efficient and accurate system for social distancing detection.
