VisionixAI

Zone-Based Computer Vision Automation
Smart actions through visual presence detection—no external sensors or hardware dependencies required.

VisionixAI is a modular computer vision platform that detects human presence within predefined spatial zones and orchestrates automated responses. By leveraging high-performance visual processing, VisionixAI delivers seamless, scalable automation for smart environments—from homes to enterprise settings.

Table of Contents

• System Overview
• Architecture Diagram
• Components
• Technologies
• Status
• License

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System Overview

Any physical space is divided into virtual grid zones. When a zone remains unoccupied for a configurable duration, VisionixAI emits control signals to power off connected devices. Upon detecting a person re-entering the zone, the system reverses the automation. All interactions rely solely on visual input.

Key features:

• Zone Partitioning: Virtual grid overlay on frames.
• Presence Detection: Combines object detection (YOLOv8) and tracking (MediaPipe).
• Noise Reduction: Temporal smoothing to prevent false triggers.
• Extensible Output: MQTT, GPIO, WebSockets, or cloud relays.

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Architecture Diagram

flowchart TD
  subgraph EntryPoint
    A1[CLI Tool]
  end

  subgraph Input
    B1[Camera Stream Handler]
    B2[Static Video Loader]
  end

  subgraph Preprocessing
    C1[Frame Normalizer]
    C2[Zone Grid Mapper]
  end

  subgraph Detection_Engine
    D1[YOLOv8 Detector]
    D2[MediaPipe Tracker]
    D3[Frame-wise Inference]
  end

  subgraph Postprocessing
    E1[Zone Occupancy Logic]
    E2[Temporal Smoothing]
  end

  subgraph Output_Layer
    F1[Signal Dispatcher]
    F2[MQTT Emitter]
    F3[GPIO / Relay Trigger]
  end

  subgraph API_Layer
    G1[FastAPI Interface]
    G2[RESTful Endpoints]
    G3[Web Dashboard]
  end

  subgraph Data_Storage
    H1[Local State]
    H2[SQLite or JSON]
  end

  subgraph DevOps_and_Tooling
    I1[Typer CLI]
    I2[Rich Terminal UI]
    I3[Logging Module]
  end

  A1 --> B1
  A1 --> B2

  B1 --> C1
  B2 --> C1

  C1 --> C2
  C2 --> D1
  C2 --> D2

  D1 --> D3
  D2 --> D3

  D3 --> E1
  E1 --> E2

  E2 --> F1
  F1 --> F2
  F1 --> F3

  A1 --> I1
  A1 --> I2
  A1 --> I3

  A1 --> G1
  G1 --> G2
  G2 --> G3

  E2 --> H1
  H1 --> H2

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Components

1. CLI Tool

• Entry point for live camera or video input.
• Built with Typer and Rich for UX.

2. Input Handlers

• Camera Stream Handler: Captures real-time webcam feed.
• Static Video Loader: Processes pre-recorded footage.

3. Frame Preprocessing

• Frame Normalizer: Resizes and standardizes frames.
• Zone Grid Mapper: Overlays a configurable grid for zone partitioning.

4. Detection Engine

• YOLOv8 Detector: High-performance object detection.
• MediaPipe Tracker: Robust human tracking across frames.
• Integrated inference pipeline for per-zone presence detection.

5. Postprocessing

• Zone Occupancy Logic: Determines zone transitions.
• Temporal Smoothing: Aggregates results to reduce noise.

6. Signal Dispatcher

• Emits automation signals via:
  • MQTT brokers
  • GPIO pins / Relay modules
  • Extendable for WebSockets or cloud relays

7. (Optional) API Layer

• Planned FastAPI interface for external integration and dashboards.

8. Data Storage

• Local State: In-memory state persistence.
• Storage Backend: SQLite or JSON for logging and replay.

9. DevOps & Tooling

• CI/CD: GitHub Actions workflows.
• Logging: Structured logs for debugging.

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Technologies

• Language: Python 3.11+
• Computer Vision: OpenCV, YOLOv8 (Ultralytics), MediaPipe
• CLI & UX: Typer, Rich
• API: FastAPI (planned)
• Signaling: MQTT, GPIO

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Status

• CLI Pipeline: Functional and modular.
• Detection Engine: YOLOv8 + MediaPipe integrated and tested.
• Signal Dispatch: MQTT signaling verified.
• API & Dashboard: In development.

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License

This project is licensed under the MIT License.