Introducing
Your new presentation assistant.
Refine, enhance, and tailor your content, source relevant images, and edit visuals quicker than ever before.
Trending searches
15
Name: Manish Dongarekar
Title: Social Distance Analyzer
Semester: IV
EXISTING SYSTEM
A
O
L
I
O
C
INTERNAL GUIDE:
Dr. Drakshaveni G
ASSISTANT PROFESSOR
DEPARTMENT OF MCA, BMSITM
ABSTRACT
ABSTRACT
ABSTRACT
Social isolation has been found to be a successful strategy in the fight against the coronavirus for slowing the spread of the illness. The approach proposed is for measuring social distancing by calculating the distance between persons in order to prevent viral proliferation. This system uses video frame input to measure the distance between people in order to limit the consequences of the pandemic. This is performed by analysing a video stream captured by a security camera. The purpose of social distancing is to diminish or eliminate COVID-19 transmission in a population by limiting interaction between potentially infected people and healthy people, or between population groups with high rates of transmission and population groups with no or low levels of transmission.
INTRODUCTION
- Providing an analyzer tool to check public areas, workplaces, schools, and colleges for any infringement of friendly distance.
- YOLO (You Just Look Once), a computation supported convolutional neural network used for differentiating evidence and measuring the distance between individuals employing groups of walkers around an area by capturing the feed from such a video, is used to find objects continually.
LITERATURE SURVEY
LITERATURESURVAY
Literature Survay
- G V Shalini, M Kavitha Margret, M J Sufiya Niraimathi, S Subashree. "Social Distancing Analyzer Using Computer Vision and Deep Learning" , Journal of Physics: Conference Series, 2021.
- M. Piccardi,“Background subtraction techniques: a review,” in 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No. 04CH37583), vol. 4. IEEE, 2004, pp. 3099–3104.
- Y. Xu, J. Dong, B. Zhang, and D. Xu, “Background modeling methods in video analysis: A review and comparative evaluation,” CAAI Transactions on Intelligence Technology, vol. 1, no. 1, pp. 43–60, 2016.
- Q. Zhao, P. Zheng, S.-t. Xu, and X. Wu, “Object detection with deep learning: A review,” IEEE transactions on neural networks and learning systems, vol. 30, no. 11, pp. 3212–3232, 2019.
- A. Krizhevsky, I. Sutskever, and G. E. Hinton, S. Guliy “Imagenet classification with deep convolutional neural networks,” in Advances in neural information processing systems, 2012, pp. 1097– 1105.
EXISTING SYSTEM
Existing System
- Combating the emerging Pandemic Corona Virus 2019 Disease (COVID-19) caused by social isolation is an effective method.
- Is to maintain bio secure bubble.
- The primary covid test is necessary.
PROPOSED SYSTEM
PROPOSED SYSTEM
Proposed System:
- The information is contained in the CCTV camera's picture and video record.
- The camera is positioned such that it records at the appropriate moment, and the clip outline was converted to a 2D squirrel's view to precisely measure the separation of each person.
- The spacing between each person in the image can now be easily calculated since it corresponds to the total number of pixels between individuals in the updated bird's perspective.
MODULES
MODULES
Modules Idetified:
- For object detection detection, YOLO.
- People detection Model.
- Module for measurement detection.
Hardware requirement:
• Hard disk: 500GB.
• RAM: 8GB.
• Processor: Intel CORE i5 and above.
• OS: Windows, Mac etc.
• Graphics card: Nvidia MX250 and above.
Software Requirement:
• Programming Language: Python
• Tools: OpenCV, YOLO. NumPy.
Functional Requirement:
- FR001 Collecting Collection of mp4 videos to track distance between humans.
- FR002 Processing Social Distance Analyzer need to process and analyze data collected from mp4 video.
- FR003 Analysing Analyses different types of object and humans in collected data.
- FR004 Reporting Automatically alerting the difference between object and humans.
- FR005 Displaying Social Distance Analyzer display distance between humans.
- FR006 Controlling The Social Distance Analyzer should be able to control the appropriate amount of collection of data, process data efficiently, report events and maintain configuration information.
Non Functional Requirement:
Security:
• Systems should be secure against unauthorized access to any of their data, unauthorized use of them or any of their components, unauthorized distribution of any of their parts, and cyber-attacks aimed at them.
• Components of the Social Distance Analyzer should ensure trusted relationships among themselves.
Compatibility
• System should be compatible with any platform like Windows, Linux, Mac OS, etc
• Dataset used to analyze should be compatible with the algorithms
Maintainability
• The system should maintain a proper flow of the execution and also continuously monitor for any errors.
Portability
• The model structure(relative) will be designed in a way that it will not cause any problems when it is transferred from one place to another.
Reusability
• Algorithms written in Social Distance Analyzer should be able to reuse by other researchers for maximum productivity and consistency.
Flowchart:
Flowchart 2:
Conclusion
Conclusion
Input:
Output
Conclusion:
- Social removing patterns are divided into "Safe" and "Risky" distance categories. Additionally, names are shown in relation to object recognition and classification. The classifier may be used for live video handling as well as to develop continuing applications. During pandemics, this device might be used in conjunction with CCTV to screen individuals.
- Since mass screening is feasible, it is typically used in densely populated areas including train stations, retail malls, roadways, schoolyards, universities, bus stops, workplaces, and restaurants.
- The most popular approach of measuring social distance using object recognisable evidence and subsequent methodologies, where each member is continually differentiated using bouncing boxes, is computerised using a model that offers a convincing consistent and profound learning-based structure.
- Paradigm is extremely persuasive and successful in detecting human gap between people and generating sane and trackable warnings.