DeepCamera — Open-Source AI Camera Skills Platform

DeepCamera's open-source skills give your cameras AI — VLM scene analysis, object detection, person re-identification, all running locally with models like Qwen, DeepSeek, SmolVLM, and LLaVA. Built on proven facial recognition, RE-ID, fall detection, and CCTV/NVR surveillance monitoring, the skill catalog extends these machine learning capabilities with modern AI. All inference runs locally for maximum privacy.

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🛡️ Introducing SharpAI Aegis — Desktop App for DeepCamera

Use DeepCamera's AI skills through a desktop app with LLM-powered setup, agent chat, and smart alerts — connected to your mobile via Discord / Telegram / Slack.

SharpAI Aegis is the desktop companion for DeepCamera. It uses LLM to automatically set up your environment, configure camera skills, and manage the full AI pipeline — no manual Docker or CLI required. It also adds an intelligent agent layer: persistent memory, agentic chat with your cameras, AI video generation, voice (TTS), and conversational messaging via Discord / Telegram / Slack.

📦 Download SharpAI Aegis →

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🗺️ Roadmap

• Skill architecture — pluggable SKILL.md interface for all capabilities
• Skill Store UI — browse, install, and configure skills from Aegis
• AI/LLM-assisted skill installation — community-contributed skills installed and configured via AI agent
• GPU / NPU / CPU (AIPC) aware installation — auto-detect hardware, install matching frameworks, convert models to optimal format
• Hardware environment layer — shared env_config.py for auto-detection + model optimization across NVIDIA, AMD, Apple Silicon, Intel, and CPU
• Skill development — 19 skills across 10 categories, actively expanding with community contributions

🧩 Skill Catalog

Each skill is a self-contained module with its own model, parameters, and communication protocol. See the Skill Development Guide and Platform Parameters to build your own.

Category	Skill	What It Does	Status
Detection	yolo-detection-2026	Real-time 80+ class detection — auto-accelerated via TensorRT / CoreML / OpenVINO / ONNX	✅
	yolo-detection-2026-coral-tpu	Google Coral Edge TPU — ~4ms inference via USB accelerator (LiteRT)	✅
	yolo-detection-2026-openvino	Intel NCS2 USB / Intel GPU / CPU — multi-device via OpenVINO (architecture)	🧪
	face-detection-recognition	Face detection & recognition — identify known faces from camera feeds	📐
	license-plate-recognition	License plate detection & recognition — read plate numbers from camera feeds	📐
Analysis	home-security-benchmark	143-test evaluation suite for LLM & VLM security performance	✅
Privacy	depth-estimation	Real-time depth-map privacy transform — anonymize camera feeds while preserving activity	✅
Segmentation	sam2-segmentation	Interactive click-to-segment with Segment Anything 2 — pixel-perfect masks, point/box prompts, video tracking	✅
Annotation	dataset-annotation	AI-assisted dataset labeling — auto-detect, human review, COCO/YOLO/VOC export for custom model training	✅
Training	model-training	Agent-driven YOLO fine-tuning — annotate, train, export, deploy	📐
Automation	mqtt · webhook · ha-trigger	Event-driven automation triggers	📐
Integrations	homeassistant-bridge	HA cameras in ↔ detection results out	📐

✅ Ready · 🧪 Testing · 📐 Planned

Registry: All skills are indexed in skills.json for programmatic discovery.

Detection & Segmentation Skills

Detection and segmentation skills process visual data from camera feeds — detecting objects, segmenting regions, or analyzing scenes. All skills use the same JSONL stdin/stdout protocol: Aegis writes a frame to a shared volume, sends a frame event on stdin, and reads detections from stdout. Every detection skill is interchangeable from Aegis's perspective.

graph TB
    CAM["📷 Camera Feed"] --> GOV["Frame Governor (5 FPS)"]
    GOV --> |"frame.jpg → shared volume"| PROTO["JSONL stdin/stdout Protocol"]

    PROTO --> YOLO["yolo-detection-2026"]
    PROTO --> CORAL["yolo-detection-2026-coral-tpu"]
    PROTO --> OV["yolo-detection-2026-openvino"]

    subgraph Backends["Skill Backends"]
        YOLO --> ENV["env_config.py auto-detect"]
        ENV --> TRT["NVIDIA → TensorRT"]
        ENV --> CML["Apple Silicon → CoreML"]
        ENV --> OVIR["Intel → OpenVINO IR"]
        ENV --> ONNX["AMD / CPU → ONNX"]

        CORAL --> LITERT["ai-edge-litert + libedgetpu"]
        LITERT --> TPU["Coral USB → Edge TPU delegate"]
        LITERT --> CPU1["No TPU → CPU fallback"]

        OV --> OVSDK["OpenVINO SDK"]
        OVSDK --> NCS2["Intel NCS2 USB"]
        OVSDK --> IGPU["Intel iGPU / Arc"]
        OVSDK --> CPU2["CPU fallback"]
    end

    YOLO --> |"stdout: detections"| AEGIS["Aegis IPC → Live Overlay + Alerts"]
    CORAL --> |"stdout: detections"| AEGIS
    OV --> |"stdout: detections"| AEGIS

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• Unified protocol — each skill creates its own Python venv or Docker container, but Aegis sees the same JSONL interface regardless of backend
• Coral TPU uses ai-edge-litert (LiteRT) with the libedgetpu delegate — supports Python 3.9–3.13 on Linux, macOS, and Windows
• Same output — Aegis sees identical JSONL from all skills, so detection overlays, alerts, and forensic analysis work with any backend

LLM-Assisted Skill Installation

Skills are installed by an autonomous LLM deployment agent — not by brittle shell scripts. When you click "Install" in Aegis, a focused mini-agent session reads the skill's SKILL.md manifest and figures out what to do:

1. Probe — reads SKILL.md, requirements.txt, and package.json to understand what the skill needs
2. Detect hardware — checks for NVIDIA (CUDA), AMD (ROCm), Apple Silicon (MPS), Intel (OpenVINO), or CPU-only
3. Install — runs the right commands (pip install, npm install, system packages) with the correct backend-specific dependencies
4. Verify — runs a smoke test to confirm the skill loads before marking it complete
5. Determine launch command — figures out the exact run_command to start the skill and saves it to the registry

This means community-contributed skills don't need a bespoke installer — the LLM reads the manifest and adapts to whatever hardware you have. If something fails, it reads the error output and tries to fix it autonomously.

🚀 Getting Started with SharpAI Aegis

The easiest way to run DeepCamera's AI skills. Aegis connects everything — cameras, models, skills, and you.

• 📷 Connect cameras in seconds — add RTSP/ONVIF cameras, webcams, or iPhone cameras for a quick test
• 🤖 Built-in local LLM & VLM — llama-server included, no separate setup needed
• 📦 One-click skill deployment — install skills from the catalog with AI-assisted troubleshooting
• 🔽 One-click HuggingFace downloads — browse and run Qwen, DeepSeek, SmolVLM, LLaVA, MiniCPM-V
• 📊 Find the best VLM for your machine — benchmark models on your own hardware with HomeSec-Bench
• 💬 Talk to your guard — via Telegram, Discord, or Slack. Ask what happened, tell it what to watch for, get AI-reasoned answers with footage.

🎯 YOLO 2026 — Real-Time Object Detection

State-of-the-art detection running locally on any hardware, fully integrated as a DeepCamera skill.

YOLO26 Models

YOLO26 (Jan 2026) eliminates NMS and DFL for cleaner exports and lower latency. Pick the size that fits your hardware:

Model	Params	Latency (optimized)	Use Case
yolo26n (nano)	2.6M	~2ms	Edge devices, real-time on CPU
yolo26s (small)	11.2M	~5ms	Balanced speed & accuracy
yolo26m (medium)	25.4M	~12ms	Accuracy-focused
yolo26l (large)	52.3M	~25ms	Maximum detection quality

All models detect 80+ COCO classes: people, vehicles, animals, everyday objects.

Hardware Acceleration

The shared env_config.py auto-detects your GPU and converts the model to the fastest native format — zero manual setup:

Your Hardware	Optimized Format	Runtime	Speedup vs PyTorch
NVIDIA GPU (RTX, Jetson)	TensorRT .engine	CUDA	3-5x
Apple Silicon (M1–M4)	CoreML .mlpackage	ANE + GPU	~2x
Intel (CPU, iGPU, NPU)	OpenVINO IR .xml	OpenVINO	2-3x
AMD GPU (RX, MI)	ONNX Runtime	ROCm	1.5-2x
Any CPU	ONNX Runtime	CPU	~1.5x
Google Coral USB Accelerator	Edge TPU .tflite	ai-edge-litert + libedgetpu	~4ms flat

Aegis Skill Integration

Detection runs as a parallel pipeline alongside VLM analysis — never blocks your AI agent:

Camera → Frame Governor → detect.py (JSONL) → Aegis IPC → Live Overlay
                5 FPS           ↓
                          perf_stats (p50/p95/p99 latency)

• 🖱️ Click to setup — one button in Aegis installs everything, no terminal needed
• 🤖 AI-driven environment config — autonomous agent detects your GPU, installs the right framework (CUDA/ROCm/CoreML/OpenVINO), converts models, and verifies the setup
• 📺 Live bounding boxes — detection results rendered as overlays on RTSP camera streams
• 📊 Built-in performance profiling — aggregate latency stats (p50/p95/p99) emitted every 50 frames
• ⚡ Auto start — set auto_start: true to begin detecting when Aegis launches

📖 Full Skill Documentation →

🔒 Privacy — Depth Map Anonymization

Watch your cameras without seeing faces, clothing, or identities. The depth-estimation skill transforms live feeds into colorized depth maps using Depth Anything v2 — warm colors for nearby objects, cool colors for distant ones.

Camera Frame ──→ Depth Anything v2 ──→ Colorized Depth Map ──→ Aegis Overlay
   (live)          (0.5 FPS)           warm=near, cool=far      (privacy on)

• 🛡️ Full anonymization — depth_only mode hides all visual identity while preserving spatial activity
• 🎨 Overlay mode — blend depth on top of original feed with adjustable opacity
• ⚡ Rate-limited — 0.5 FPS frontend capture + backend scheduler keeps GPU load minimal
• 🧩 Extensible — new privacy skills (blur, pixelation, silhouette) can subclass TransformSkillBase

Runs on the same hardware acceleration stack as YOLO detection — CUDA, MPS, ROCm, OpenVINO, or CPU.

📖 Full Skill Documentation → · 📖 README →

📊 HomeSec-Bench — How Secure Is Your Local AI?

HomeSec-Bench is a 143-test security benchmark that measures how well your local AI performs as a security guard. It tests what matters: Can it detect a person in fog? Classify a break-in vs. a delivery? Resist prompt injection? Route alerts correctly at 3 AM?

Run it on your own hardware to know exactly where your setup stands.

Area	Tests	What's at Stake
Scene Understanding	35	Person detection in fog, rain, night IR, sun glare
Security Classification	12	Telling a break-in from a raccoon
Tool Use & Reasoning	16	Correct tool calls with accurate parameters
Prompt Injection Resistance	4	Adversarial attacks that try to disable your guard
Privacy Compliance	3	PII leak prevention, illegal surveillance refusal
Alert Routing	5	Right message, right channel, right time

Results: Local vs. Cloud vs. Hybrid

Running on a Mac M1 Mini 8GB: local Qwen3.5-4B scores 39/54 (72%), cloud GPT-5.2 scores 46/48 (96%), and the hybrid config reaches 53/54 (98%). All 35 VLM test images are AI-generated — no real footage, fully privacy-compliant.

📄 Read the Paper · 🔬 Run It Yourself · 📋 Test Scenarios

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📦 More Applications

Legacy Applications (SharpAI-Hub CLI)

These applications use the sharpai-cli Docker-based workflow. For the modern experience, use SharpAI Aegis.

Application	CLI Command	Platforms
Person Recognition (ReID)	sharpai-cli yolov7_reid start	Jetson/Windows/Linux/macOS
Person Detector	sharpai-cli yolov7_person_detector start	Jetson/Windows/Linux/macOS
Facial Recognition	sharpai-cli deepcamera start	Jetson/Windows/Linux/macOS
Local Facial Recognition	sharpai-cli local_deepcamera start	Windows/Linux/macOS
Screen Monitor	sharpai-cli screen_monitor start	Windows/Linux/macOS
Parking Monitor	sharpai-cli yoloparking start	Jetson AGX
Fall Detection	sharpai-cli falldetection start	Jetson AGX

📖 Detailed setup guides →

Tested Devices

• Edge: Jetson Nano, Xavier AGX, Raspberry Pi 4/8GB
• Desktop: macOS, Windows 11, Ubuntu 20.04
• MCU: ESP32 CAM, ESP32-S3-Eye

Tested Cameras

• RTSP: DaHua, Lorex, Amcrest
• Cloud: Blink, Nest (via Home Assistant)
• Mobile: IP Camera Lite (iOS)

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🏗️ Architecture

Complete Feature List →

🤝 Support & Community

• 💬 Slack Community — help, discussions, and camera setup assistance
• 🐛 GitHub Issues — technical support and bug reports
• 🏢 Commercial Support — pipeline optimization, custom models, edge deployment

Contributions