ai-based-security-camera
https://github.com/gowthamselvaraj327/ai-based-security-camera
Science Score: 44.0%
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✓CITATION.cff file
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✓codemeta.json file
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✓.zenodo.json file
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○DOI references
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○Academic publication links
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○Institutional organization owner
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○Scientific vocabulary similarity
Low similarity (7.8%) to scientific vocabulary
Repository
Basic Info
- Host: GitHub
- Owner: gowthamselvaraj327
- License: gpl-3.0
- Language: Python
- Default Branch: main
- Size: 24.5 MB
Statistics
- Stars: 1
- Watchers: 2
- Forks: 2
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
AI-Based-Security-Camera
An AI-based security camera is a CCTV camera that uses artificial intelligence algorithms to analyze video footage and identify potential security threats in real-time. These cameras are equipped with advanced features such as object recognition, motion detection, and object detection, which allow them to detect suspicious behavior and alert security personnel immediately.
We detect if any person or human identified in the area then we sent a email notification to the person who owns the area.
AI-based security cameras can also learn and adapt to their surroundings over time, improving their ability to identify and flag potential security risks. Additionally, some models may also be able to automatically track moving objects, zoom in on suspicious activity, and even communicate with other security systems to coordinate a response.
Overall, an AI-based security camera provides an advanced level of security and surveillance, making it an ideal solution for businesses, public spaces, and other high-risk environments.
Live Security system, a simple E-mail based security system that sends notification on instance of a personal property or private property invasion by a person. Main objective based on quick response to a personal or restricted area invasion due to lack in simple day-to-day live security system in present situation. The focus of the model is to be a quick and simple E-mail notification system that should be usable by anyone and maintain ease of use.
Project Image:
Test Images:
For detection of object (person) we use yolov5 from darknet which combines bounding box prediction and object classification into a single end to end differentiable network with the help of an edge computing device which plays a major role. These two components enable us in detecting the invaders or unauthorized persons through CCTV. We store the detected results in a storage device at regular interval of time and also we backup the video footage in storage devices.
Owner
- Name: GOWTHAM S
- Login: gowthamselvaraj327
- Kind: user
- Location: Sathyamangalam
- Company: Bannari Amman Institute of Technology
- Website: gowthamselvaraj.me
- Repositories: 6
- Profile: https://github.com/gowthamselvaraj327
I am a passionate and experienced React.js and React Native developer with a strong background in web and mobile app development. I have a deep understanding of
Citation (CITATION.cff)
cff-version: 1.2.0
preferred-citation:
type: software
message: If you use YOLOv5, please cite it as below.
authors:
- family-names: Jocher
given-names: Glenn
orcid: "https://orcid.org/0000-0001-5950-6979"
title: "YOLOv5 by Ultralytics"
version: 7.0
doi: 10.5281/zenodo.3908559
date-released: 2020-5-29
license: GPL-3.0
url: "https://github.com/ultralytics/yolov5"
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Dependencies
- nvcr.io/nvidia/pytorch 22.11-py3 build
- Pillow >=7.1.2
- PyYAML >=5.3.1
- gitpython *
- ipython *
- matplotlib >=3.2.2
- numpy >=1.18.5
- opencv-python >=4.1.1
- pandas >=1.1.4
- psutil *
- requests >=2.23.0
- scipy >=1.4.1
- seaborn >=0.11.0
- tensorboard >=2.4.1
- thop >=0.1.1
- torchvision >=0.8.1
- tqdm >=4.64.0