https://github.com/jacobpclouse/hackrpi24-adaptive-threat-security-system

Online Security System
https://github.com/jacobpclouse/hackrpi24-adaptive-threat-security-system

Last synced: 4 days ago
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Online Security System

• Host: GitHub
• URL: https://github.com/jacobpclouse/hackrpi24-adaptive-threat-security-system
• Owner: jacobpclouse
• License: mit
• Created: 2024-11-09T16:12:19.000Z (7 days ago)
• Default Branch: main
• Last Pushed: 2024-11-09T16:35:49.000Z (7 days ago)
• Last Synced: 2024-11-09T17:26:43.829Z (7 days ago)
• Language: Python
• Size: 2.93 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Crime Catcher  

> Safety Through Vigilance!

#### REPO: HackRPI24-Adaptive-Threat-Security-System - Submitted via Devpost to HackRPI 2024 Urban Upgrades

[![GitHub contributors](https://img.shields.io/github/contributors/jacobpclouse/HackRPI24-Adaptive-Threat-Security-System.svg)]("https://github.com/jacobpclouse/HackRPI24-Adaptive-Threat-Security-System/graphs/contributors") 





## What it does

Crime Catcher is a solution to keeping our urban spaces safe and secure.

It is an online surveillance system where a central server receives camera feeds from remote clients and uses AI to determine if weapons or dangerous objects are in frame. The model is GPU-accelerated for efficient processing.

If a dangerous object is detected, the backend automatically sends an email alert to a trusted contact, ensuring every second counts.

Additionally, we save and catalog videos and metadata using an SQLite database. This data can be browsed for review and analysis after the fact.

### Other Features:

- Clients will automatically try to reconnect if they get disconnected from the server (they will disconnect after 5 attempts).

- We integrate time stamps into the video streams such as frame rate, source IP, source building, etc.

- Users can specify an email address in the server to receive alerts about dangerous objects and weapons.

- We used ttk bootstrap to create beautiful interfaces on both the client and server in part 1.

- For Part 2 (the vue.js/quasar and flask dashboard) we have a login system that enforces users to login before they can access the video metadata, ensuring the system is attributable

- We have motion detection in our server stream so only eventful data is saved to disk, conserving bandwidth

## Challenges we ran into

- Working with Tkinter's documentation and using the CUDA framework in our server for GPU acceleration

## Accomplishments that we're proud of

- Motion detection: Creating logic that pauses recording on the server if there is no motion going on in the camera frame.

- AI weapon detection: Utilizing machine learning to detect and locate weapons.

- Video feed centralization: Gathering multiple camera feeds to effectively track and stop bad actors.

## What we learned

Tkinter documentation is difficult to understand, and we learned how to integrate an AI model into our Tkinter server and how to best select a pre trained model to suite our needs. We also learned more about YOLO and model training.

## What's next for Crime Catcher

After our MVP, we want to iterate and add new features like remote client activation in the next sprint.

## Sources:

- Text on Video with OpenCV: https://www.geeksforgeeks.org/python-opencv-write-text-on-video/

- Ascii art generated using: http://patorjk.com/software/taag/#p=display&f=Graffiti&t=Type%20Something%20

- Guns detection model from: https://www.kaggle.com/code/ahmedgaitani/guns-object-detection-code