https://github.com/munas-git/automated-attendance-and-attention-tracker
https://github.com/munas-git/automated-attendance-and-attention-tracker
Last synced: 8 months ago
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- Host: GitHub
- URL: https://github.com/munas-git/automated-attendance-and-attention-tracker
- Owner: munas-git
- License: mit
- Created: 2024-01-23T19:06:08.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-07-07T12:09:26.000Z (almost 2 years ago)
- Last Synced: 2025-08-12T16:36:20.107Z (10 months ago)
- Language: Python
- Size: 2.79 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
##### Project Status: Incomplete (Temporarily on Pause Till Further Notice).
# Project Title: Automated Attendance and Attention Tracker.
## Project Description.
**Background** :
Traditional attendance tracking methods are prone to various challenges, such as students forgetting to log in, loss of mobile phones/laptops, no battery on device to register attendance on campus connect, dishonest practices, etc. The proposed system addresses these issues by automating the attendance process using state-of-the-art facial recognition and attention detection technologies. Additionally, the system aims to provide valuable insights into student engagement and interest levels during lectures.
**AI Functionalities**:
1. Face detect & verification
2. Automate attendance registration of on verified face.
3. Audience tiredness detection
4. Audience Focus/distraction detection
5. Audience participation detection
**BI Functionalities (Displayed on Tableau dashboard)**
1. Analytics on number of students present
2. Analytics of number of registered and unregistered students
3. Analytics of early class leavers %
4. Analytics on interest rate at different times during lecture (Highlights peak interest and loss of interest)
5. Analytics of attention rate at differrent rimes during lecture (Highlights peak attention and loss of attention)
6. Analytics of tiredness levels throughout lectures. (Highlights peak tiredness time)
**Technologies Used**
1. ***Programming Languages***
- Python (Version: )
- SQL
2. ***Tools***
- Tableau
- MongoDB
3. ***Top Libraries (Find the rest in requirements file)***
- OpenCV
## How To Run Project On Your Device (Windows x64).
1. Open your terminal, navigate to desired directory and run *"git clone https://github.com/munas-git/Automated-Attendance-and-Attention-Tracker.git"*
2. Open the folder/directory in your desired IDE e.g. Visual Studio Code, PyCharm, Etc. and navigate to the folder/directory in terminal as well.
3. **Settinhg up project:**
- **Alternative 1:** Create your virtual environment and install all dependencies listed in *resuirements.txt*
- **Alternative 2:** Run run the bash script ***"setup.sh"*** (creates virtual environment, installs dependencies, activates virtual environment and starts system.)
- **Alternative 3:** Run the python script ***"setup.sh"*** (setup.sh python version.)
4.