Home Monitoring System

Tech Stack

• Backend
  • Python, Django, MySQL, OpenCV, nginx, ECS
• Frontend
  • Vue, Axios, Element plus, Rellax, Kinesis, Echarts
• Models
  • PyTorch, Tensorflow

Module

• User Management
  • ORM framework
  • model: face recognition + blinking detection for dynamic face login
• Pedestrian and Pet Recognition
  • model: object detection
• Emotion Recognition
  • model: micro expression recognition + emotion recognition
• Intrusion Detection
  • model: face recognition
  • Video Storage: H.264 encoded MP4 video before and after intrusion
• Model Service
  • model: image captioning + 3D to 2D model + fall detection
• Disabilities Friendly
  • model: gesture recognition
  • Self-defined gestures

Docs

Requirement Analysis Document, Design Document, Testing Document, User Manual.

Project Structure

'family_monitor_server/' is the project container.

Under 'user/' are functionalities related to users.

Under 'recognition/' are functionalities related to models.

Under 'model/' are various models. If a model contains multiple files, please create a subfolder.

Installation Steps

Below requires the command line working directory to be in the root folder of this repository.

Prepare Python Virtual Environment

Create Virtual Environment

python -m venv venv

Activate Virtual Environment

Windows

.\venv\Scripts\activate.bat

Linux

source ./venv/bin/activate

When the command prompt displays (venv), it signifies that the virtual environment has been successfully activated.

Install Required Dependencies

One-click installation via requirements.txt, after entering the virtual environment in the previous step (venv), run the following command in the command line

pip install -r requirements.txt

Database

Copy db_setting.cnf to the project root directory.

Model Configuration

Copy the models sent in the group to the specified directory.

• Place model_U.pth under model/emotional_recognition/
• After extracting model.zip, place the four files in model/microexpression_recognition/model/
• Place shape_predictor_68_face_landmarks.dat under model/isLive/gaze_tracking/trained_models/
• Place checkpoint.pt under model/gaze_vector/
• Place weight389123791.pth and weight493084032.pth under /model/image_caption/models/
• Place the 'Models' folder from the extracted Fall_Models under /model/fall-detect-track/