Machina - Video Surveillance System

A simple-to-use AI-powered video surveillance system that can detect and track objects in real-time from cameras.

What does it do?

Machina watches video feeds and automatically detects objects like people, cars, and other things you care about. It keeps track of each object and can record video or take screenshots when needed.

Key Features:

• Runs completely offline - no internet required
• Records video and takes snapshots with one key press
• Real-time object detection and tracking
• Zoom and pan controls with smart object tracking
• Crowd detection and clustering
• Instant replay system with 10-second buffer
• Multiple resolution presets and fullscreen support
• Military mode for enhanced detection
• Smart frame skipping and performance optimization

Quick Start

1. Install Python 3.8+ and make sure you have a webcam or camera stream
2. Download and install:

git clone https://github.com/PsyChip/machina
cd machina
pip install -r requirements.txt

3. Torch library should be installed manually:

pip uninstall torch torchvision torchaudio
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

4. Run the program:

python app.py

How to Use

Setting up your camera

Edit the top of app.py to change your video source:

• Webcam: Set rtsp_stream = 0
• IP Camera: Set rtsp_stream = "rtsp://your-camera-url"
• Video File: Set rtsp_stream = "path/to/your/video.mp4"

Controls

Key	What it does
Q	Quit
R	Start/Stop recording
S	Take screenshot
F	Reset window to stream resolution
M	Toggle military mode (enhanced detection)
Space	Skip frames
Backspace	Toggle replay mode
1	Resize to 640x480
2	Resize to 800x600
3	Resize to 1024x768
4	Resize to 1280x800
5	Resize to 1920x1080
6	Reset to original stream size
Enter	Toggle fullscreen mode
Esc	Exit fullscreen mode

Mouse Controls:

• Scroll wheel: Zoom in/out
• Right-click + drag: Move around when zoomed
• Left-click + drag: Draw selection box

What gets detected

By default, Machina looks for daily objects you can see on street: people, cars, motorcycles etc. You can change what it looks for by editing the classlist in app.py.

Saved Files

• Screenshots: Saved in snapshots/ folder
• Recordings: Saved in recordings/ folder
• Object images: Saved in elements/ folder

Troubleshooting

Program runs slowly?

• Try pressing M to enable military mode for optimized processing
• Make sure you have a graphics card and installed the CUDA version above
• Use number keys (1-6) to adjust resolution for better performance

Can't connect to camera?

• Check your camera URL or make sure webcam is plugged in
• Try changing rtsp_stream = 0 for built-in webcam

Need help? The program shows what it's doing in the terminal window. Look there for error messages.

Dependencies

Core Libraries

opencv-python==4.10.0.84  # Computer vision operations
ultralytics>=8.3.1        # YOLO model implementation
torch                      # Deep learning framework
scikit-learn              # DBSCAN clustering for crowd detection
numpy                     # Numerical computations
dill>=0.3.9               # Object serialization
pickleshare>=0.7.5        # Caching utilities

Advanced Features

Zoom and Pan System

• Scroll wheel to zoom in/out (up to 6x zoom)
• Right-click and drag to pan around when zoomed
• Smart object tracking preservation during zoom operations
• Automatic YOLO processing optimization during zoom

Military Mode

• Enhanced detection mode with grayscale processing
• Optimized for person and vehicle detection
• Reduced frame skipping for maximum accuracy
• Toggle with M key

Replay System

• Automatic 10-second rolling buffer (~300 frames at 30fps)
• Instant replay with Backspace key
• Visual "REPLAY" indicator during playback
• No YOLO processing during replay for smooth playback

Resolution Management

• Multiple preset resolutions (640x480 to 1920x1080)
• Dynamic YOLO input size adjustment
• Fullscreen support with aspect ratio preservation
• Original stream size restoration

System Requirements

• Python 3.8+
• CUDA compatible GPU with at least 2GB VRAM (Developed on NVIDIA RTX 2060)
• At least 16GB RAM, 32GB recommended to run in background

---

Created by **PsyChip** for experimental purposes Repository autonomously maintained by **SIGMA AI** - https://sigma.ml