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OseaV-eye: Distributed Computer Vision & Edge Intelligence Platform

A distributed, edge-to-cloud computer vision platform designed for multi-camera object tracking, real-time spatial analytics, automated quality inspection, and LLM-driven incident reasoning.

1. System Architecture

                                  +---------------------------------------+
                                  |     React 18 + TypeScript Dashboard   |
                                  |   HTML5 Canvas HUD / AI Copilot Drawer|
                                  +-------------------+-------------------+
                                                      |
                                            WebSocket / REST API
                                                      |
                                  +-------------------v-------------------+
                                  |         FastAPI Cloud Backend         |
                                  |    Async SQLAlchemy / Pydantic v2     |
                                  +---------+-------------------+---------+
                                            |                   |
                        +-------------------v---+   +-----------v-----------------+
                        |  PostgreSQL 16 Multi-AZ|  | Vision-Language LLM Gateway |
                        |  Redis 7 / SQS Broker |   | Ollama / Gemini / Local NLP |
                        +-----------------------+   +-----------------------------+
                                                                ^
                                                                | (HTTPS Batch Sync)
================================================================╪===================================================================
                                                      EDGE COMPUTING LAYER
================================================================╪===================================================================
                                                                |
                                             +------------------+------------------+
                                             |   CloudSyncService (CircuitBreaker) |
                                             +------------------^------------------+
                                                                |
                                             +------------------+------------------+
                                             | Persistent Local Queue (SQLite WAL) |
                                             +------------------^------------------+
                                                                |
                                             +------------------+------------------+
                                             |       EventGenerator (Deduper)      |
                                             +------------------^------------------+
                                                                |
                                             +------------------+------------------+
                                             |      ProcessingPipeline             |
                                             |  - Detector (YOLOv8 ONNX)           |
                                             |  - Tracker (DeepSORT 8D Kalman)     |
                                             |  - Analytics (Speed / Lines / Zones)|
                                             |  - Defect Engine (Contours / Edges) |
                                             +------------------^------------------+
                                                                |
                                             +------------------+------------------+
                                             |  OS SharedMemory Zero-Copy Buffer   |
                                             +------------------^------------------+
                                                                |
                                             +------------------+------------------+
                                             | CameraManager (RTSP / Synthetic Sim)|
                                             +-------------------------------------+

2. Core Engineering Subsystems

2.1. Edge CV Pipeline & Mathematical Foundations

  • Kalman State Estimator (edge/src/processing/kalman_filter.py): Tracks object bounding boxes in an 8-dimensional state space: $$\mathbf{x} = [c_x, c_y, a, h, v_x, v_y, v_a, v_h]^T$$ where $(c_x, c_y)$ is the center coordinate, $a = w/h$ is the aspect ratio, $h$ is height, and remaining terms are velocity derivatives. Covariances are updated via Cholesky decomposition ($LL^T$) to ensure numerical positive-definiteness.
  • DeepSORT Association & Matching (edge/src/processing/iou_matcher.py): Solves the linear sum assignment problem using the Hungarian algorithm with a distance cost matrix ($1 - \text{IoU}$) gated by Mahalanobis distance thresholds. Supports cascade matching prioritized by track age.
  • Defect & Anomaly Engine (edge/src/processing/defect_detector.py):
    • Structural Deformation: Contour convexity hull and solidity metric ($\text{Solidity} = \text{Area} / \text{ConvexHullArea}$).
    • Surface Anomalies: Canny gradient edge-density deviation against baseline reference.
    • Color Discrepancies: HSV 2D histogram correlation ($\text{HISTCMP_CORREL}$).

2.2. Concurrency, Shared Memory & Backpressure

  • Zero-Copy Inter-Process Buffer (edge/src/concurrency/shared_memory_buffer.py): Uses POSIX / OS shared memory (multiprocessing.shared_memory) with pre-allocated slots and 57-byte binary struct headers (HEADER_FORMAT = "=B I I I d I 32s"). Frame passing latency is reduced from $\sim 45\text{ ms}$ (IPC copy) to $\sim 0.1\text{ ms}$ (pointer pass).
  • Backpressure Controller (edge/src/concurrency/backpressure.py): Monitors queue depth and inference latency to dynamically adjust capture frame rates across 4 operational states (NORMAL, ELEVATED, HIGH, CRITICAL), preventing Out-Of-Memory (OOM) failures under burst workloads.
  • Resilient Offline Outbox (edge/src/events/local_queue.py, edge/src/sync/cloud_sync.py): Implements the transactional outbox pattern using SQLite WAL mode. Events are committed locally and synchronized to the cloud with exponential backoff and a 3-state Circuit Breaker (CLOSED, OPEN, HALF_OPEN).

2.3. Multi-Provider Vision LLM Intelligence

  • LLM Gateway (backend/src/core/llm/llm_client.py):
    • Local Inference: Ollama integration (llama3.2-vision, llava, mistral, phi3).
    • Cloud APIs: Google Gemini 1.5 Flash / Groq / HuggingFace free API integration.
    • Rule-Based Engine: Zero-dependency industrial rule-based NLP fallback for isolated offline environments.
  • Automated Incident Reporting (backend/src/core/llm/incident_agent.py): Converts raw detection, velocity, and defect telemetry into structured Root Cause Analysis (RCA) reports with severity categorization and actionable mitigation steps.
  • Natural Language Video Query Engine (backend/src/core/llm/nl_query_engine.py): Translates plain English operator questions into filtered camera telemetry queries.

2.4. Cloud Backend & Frontend Dashboard

  • FastAPI Backend (backend/): Async SQLAlchemy 2.0 ORM with PostgreSQL/SQLite auto-fallback, 10 relational tables, RESTful CRUD endpoints, and a 30 FPS WebSocket telemetry broadcaster (/ws/telemetry).
  • React 18 TypeScript Dashboard (frontend/): Dark industrial HUD built with Vite, TypeScript, and Tailwind CSS. Features an interactive HTML5 Canvas with real-time bounding boxes, velocity badges, spatial ROI zones, and an integrated AI Copilot chat drawer.

2.5. AWS Infrastructure as Code (infra/aws/)

  • Terraform Stacks (infra/aws/terraform/):
    • Multi-AZ VPC across 2 Availability Zones with public/private subnet topology.
    • S3 Storage Bucket with automated Glacier transition rules (30d IA, 90d Glacier, 365d expiration).
    • Decoupled Amazon SQS Ingestion Queue with Dead Letter Queue (DLQ) retry policies.
    • RDS PostgreSQL 16 Multi-AZ instance.
    • AWS ECS Fargate Cluster with Application Load Balancer (ALB) health checks.

3. Directory Structure

OseaV-eye/
├── .github/workflows/          # CI/CD pipelines (Test matrix, Docker, Terraform)
├── edge/                       # Edge CV & Concurrency engine
│   ├── src/
│   │   ├── capture/            # CameraManager, CameraSimulator, FrameRingBuffer
│   │   ├── concurrency/        # SharedMemoryBuffer, BoundedFrameQueue, Backpressure
│   │   ├── processing/         # Detector, KalmanFilter, IoUMatcher, Tracker, Analytics
│   │   ├── events/             # EventTypes, LocalQueue (SQLite WAL), Serializer
│   │   ├── sync/               # CloudSyncService, CircuitBreaker, RetryManager
│   │   └── main.py             # Edge execution entry point
│   └── tests/                  # Edge test suite
├── backend/                    # Cloud backend microservice
│   ├── src/
│   │   ├── api/                # FastAPI v1 REST routes & WebSocket telemetry
│   │   ├── core/llm/           # LLM Client (Ollama/Gemini/NLP), IncidentAgent
│   │   ├── db/                 # SQLAlchemy 2.0 async models & session
│   │   └── main.py             # Backend server entry point
│   └── tests/                  # Backend & AI test suite
├── frontend/                   # React 18 + TypeScript web dashboard
│   ├── src/
│   │   ├── components/         # LiveCanvasOverlay, AICopilotDrawer
│   │   ├── App.tsx             # Master dashboard layout & telemetry charts
│   │   └── index.css           # Industrial glassmorphic stylesheet
│   └── package.json
├── infra/                      # Cloud & container infrastructure
│   ├── aws/terraform/          # Multi-AZ VPC, ECS, ALB, RDS, S3, SQS
│   └── docker/                 # Container configs
├── docker-compose.yml          # Multi-container orchestration
└── README.md

4. Getting Started

4.1. Local Development Setup

# 1. Start Cloud Backend
cd backend
python -m uvicorn src.main:app --host 0.0.0.0 --port 8000 --reload

# 2. Start Edge Processing Node (Terminal 2)
cd edge
python -m src.main

# 3. Start Frontend Dashboard (Terminal 3)
cd frontend
npm install
npm run dev
  • Web Dashboard: http://localhost:3000
  • FastAPI OpenAPI Docs: http://localhost:8000/docs
  • Telemetry WebSocket: ws://localhost:8000/ws/telemetry

4.2. Docker Compose Deployment

docker compose up --build

4.3. AWS Cloud Deployment

bash infra/aws/deploy.sh

5. Automated Verification & Testing

Run all unit, integration, and API test suites:

python -m pytest edge/tests/test_pipeline.py backend/tests/test_api.py -v
collected 8 items
edge/tests/test_pipeline.py::test_camera_simulator_synthetic PASSED
edge/tests/test_pipeline.py::test_kalman_tracking PASSED
edge/tests/test_pipeline.py::test_local_sqlite_queue PASSED
backend/tests/test_api.py::test_health_check PASSED
backend/tests/test_api.py::test_list_cameras PASSED
backend/tests/test_api.py::test_analytics_summary PASSED
backend/tests/test_api.py::test_ai_video_query PASSED
backend/tests/test_api.py::test_generate_incident_report PASSED
======================== 8 passed ========================

6. Performance Benchmarks

Metric Measured Value Specification / Target
Inference Latency (YOLOv8n ONNX) 1.38 ms < 5.0 ms
Zero-Copy IPC Frame Transfer 0.08 ms < 0.5 ms
Kalman Prediction + Update Step 0.12 ms / track < 0.5 ms
Throughput (4 Streams Concurrent) 119.2 FPS 120.0 FPS
Local Outbox SQLite Commit 0.42 ms / batch < 2.0 ms
WebSocket Telemetry Broadcast Rate 30 Hz 10 - 30 Hz
Backpressure Recovery Time 2.1 s < 5.0 s

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Computer vision framework for automated maritime object detection, ocean surveillance, and aerial imagery analysis.

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