Best Self Hosted Alternatives to Ring Protect

Frigate is an open source network video recorder (NVR) for IP cameras built around real-time, local AI object detection. It processes camera feeds on your own hardware to reduce false positives and enable fast, searchable event review without relying on cloud analysis.

Key Features

• Real-time object detection using local accelerators (GPU/TPU) to distinguish people, cars, and other objects from motion
• Efficient motion detection to decide when and where to run inference, minimizing resource usage
• Continuous (24/7) recording and event recording with retention policies based on detected objects
• RTSP restreaming to reduce the number of direct connections to cameras
• Low-latency live viewing with WebRTC and MSE support
• Zone-based filtering and object tracking to refine events and alerts
• MQTT integration for automations and interoperability, plus tight Home Assistant integration via a custom component

Use Cases

• Build a privacy-preserving home security camera system with local AI detections and recording
• Trigger smart-home automations (lights, alarms, notifications) based on specific detected objects via MQTT/Home Assistant
• Reduce time spent reviewing footage by focusing on object-based events instead of raw motion

Limitations and Considerations

• Best results typically require a supported AI accelerator (GPU/TPU); CPU-only setups may have limited throughput
• Requires careful camera and zone/mask configuration to balance performance, accuracy, and storage usage

Frigate combines high-performance local detection with recording and review workflows to create a customizable, locally controlled security camera platform. It is especially well-suited for users who want low-latency viewing and automation-friendly integrations while keeping video processing on-premises.

ZoneMinder is a free, open-source video surveillance software for Linux that provides capture, analysis, recording and monitoring for cameras attached to a Linux machine. It supports analog, USB and IP cameras and can scale from a single camera to large deployments.

Key Features

• Web-based interface accessible from any internet-connected device, with Android and iOS apps via zmNinja
• Uses any analog or IP camera; supports night vision and high resolution cameras
• Fully on-premises data ownership with upcoming optional cloud-enhanced features
• Scalable from single-camera home setups to multi-server enterprise deployments, with GPU-accelerated processing
• APIs and third-party integrations, including popular community tools like zmNinja
• AI-powered detection through third-party modules such as EventServer and zmMagik for real-time object/person detection and blended event summaries
• Active, open-source development maintained by a dedicated community

Use Cases

• Home security and remote monitoring for residences and small offices
• Theft prevention and industrial/commercial security deployments
• Integrations with AI analytics modules to augment surveillance workflows

Conclusion: ZoneMinder provides a scalable, open-source surveillance solution for Linux with broad camera support, a web interface, and extensible analytics via third-party tools. Its flexible architecture makes it suitable for home, SMB, and industrial security scenarios.

Scrypted is an open-source video integration and NVR platform that collects, rebroadcasts, and records camera streams. It uses a plugin architecture to connect many camera brands and export low-latency streams to HomeKit, Google Home, Alexa, Home Assistant, and web/mobile clients.

Key Features

• Plugin-based architecture for broad camera and service support, including manufacturer plugins, RTSP/FFmpeg, and ONVIF.
• Low-latency rebroadcasting and stream management for HomeKit Secure Video, Google Home, Alexa, and web clients.
• Scrypted NVR plugin provides 24/7 recording, clip management, and smart detection pipelines.
• Detection and analysis plugins (OpenCV, TensorFlow-Lite compatible plugins) for motion, object, face, and license-plate sensors.
• Multi-platform deployment: desktop apps with hardware acceleration, Docker/container images, and service installs.
• Rebroadcast and transcoding pipeline that uses native media tools to transcode and adapt streams for different destinations.
• Developer-focused tooling and SDK for building/debugging plugins in TypeScript/Node.js.

Use Cases

• Export local IP camera feeds into HomeKit Secure Video for iOS/macOS integration and recording.
• Centralized NVR recording and smart detections for home or small business camera fleets.
• Bridge integrations to stream and control cameras across Alexa, Google Home, and Home Assistant.

Limitations and Considerations

• Desktop app features that expose hardware-accelerated capabilities require a paid desktop license for full hardware acceleration support.
• Advanced AI/detection plugins may require additional native dependencies (TensorFlow Lite, OpenCV) and significant CPU/GPU resources to run reliably on lower-end hosts.
• Certain remote features and some voice-assistant integrations rely on the platform's cloud component for remote connectivity; offline-only setups may require additional configuration.

Scrypted is focused on extensible, low-latency video integration and automation with a strong plugin ecosystem and multiple deployment options. It is suitable for power users and integrators who need flexible streaming, recording, and detection capabilities across consumer smart-home platforms.

Viseron is a self-hosted, local-only network video recorder (NVR) that combines IP camera recording with AI-powered computer vision. It processes camera streams on your own hardware to detect events like motion, objects, faces, and other signals useful for video surveillance.

Key Features

• RTSP/IP camera ingestion for network video capture
• Motion detection to trigger recordings and events
• AI object detection (commonly YOLO-based) with hardware acceleration options
• Face recognition and support for additional vision-based detectors (e.g., license plate recognition)
• Modular, component-based architecture to enable/disable functionality
• Built-in web interface for configuration and management
• Container-first deployment intended to run via Docker

Use Cases

• Home or small office video surveillance with local processing and storage
• Event-based recording and review (motion/object/face-triggered clips)
• GPU/Edge TPU accelerated computer vision pipelines for multiple cameras

Limitations and Considerations

• AI features may require capable hardware (GPU or Edge TPU) for best performance at higher camera counts
• Feature availability depends on enabled components and configured detectors

Viseron is a strong fit when you want a privacy-preserving NVR with advanced computer vision features and flexible, modular configuration. It is designed to keep video and analytics local while still providing modern AI-assisted surveillance capabilities.

Unblink is an open-source AI camera monitoring application that separates capture, routing, and vision inference into relay, node, and worker components. It forwards camera streams from private networks to a public relay and broadcasts frame events to whitelisted AI workers which return detections, summaries, and alerts stored for later search.

Key Features

• Federated architecture with three roles: Relay (public router/multiplexer), Node (private network proxy), and Worker (AI vision processors).
• WebSocket-based relay protocol with multiplexed logical bridges for nodes, workers, and browser clients.
• Supports common camera transports including RTSP and MJPEG and exposes an HTTP API for browser clients.
• Frame extraction and a computer-vision event bus that emits FrameEvent and FrameBatchEvent messages for workers to consume.
• Workers download frame binaries and emit bidirectional results (detections, summaries, alerts) that are stored and searchable.
• CBOR used for protocol message encoding and a minimal protocol design focused on separation of concerns and efficient forwarding of raw bytes.
• Configurable node that creates a local config file and can be installed via the Go toolchain; runtime and deployment are intended to be self-hosted and extensible.

Use Cases

• Add AI-powered detection, summarization, and alerting to home, office, or NVR camera fleets while keeping cameras on local networks.
• Run custom vision models (including VLMs) as federated workers to perform specialized inference tasks and push structured results back to the system.
• Build privacy-conscious monitoring workflows where raw bytes are proxied and inference can be performed on dedicated or self-hosted worker infrastructure.

Limitations and Considerations

• The project provides components (relay, node, worker) but expects operators to run or host the relay or use an available public relay; no official hosted SaaS is bundled with the repository.
• Worker inference for modern vision-language models can require GPU resources and model-serving infrastructure; deploying large VLMs may need specialized hardware and serving stacks.
• The system forwards raw camera bytes through the node/relay and uses CBOR for messages; integrators should plan for network, TLS, and operational setup when exposing relays and APIs.

Unblink is focused on modular, federated camera monitoring and AI inference, enabling teams to integrate custom vision workers and store searchable event results. It is suitable for projects that require on‑prem camera access with cloud‑reachable coordination and extensible worker-based inference.

Double Take is a unified web UI and REST API for processing, reviewing, and training images used for facial recognition. It abstracts differences between multiple face detection/recognition backends and provides a single workflow for matches, unknowns, and subject training.

Key Features

• Web UI for reviewing matches and managing training/untraining images per subject
• REST API for submitting images and retrieving stored match/train/latest images
• Works with multiple detectors (for example CompreFace, DeepStack, CodeProject.AI Server, Amazon Rekognition, Facebox)
• NVR integration support (notably Frigate) to process snapshots and events
• MQTT subscribe/publish for automation workflows and Home Assistant discovery
• Optional authentication to protect the UI and API, with access tokens
• Scheduling controls to disable detection during defined time windows
• Optional image preprocessing using OpenCV

Use Cases

• Add facial recognition review and training to Frigate-based home video surveillance
• Publish recognition results over MQTT to trigger Home Assistant automations and notifications
• Provide a single API façade when switching or testing different face recognition backends

Limitations and Considerations

• Requires an external detector service; Double Take orchestrates and manages workflows rather than performing recognition entirely on its own
• Recognition accuracy and performance depend heavily on the chosen detector and camera image quality

Double Take is best suited for users who want a consistent UI and automation-friendly API for facial recognition workflows, especially when integrating NVR events and MQTT-based home automation. It simplifies multi-backend setups and centralizes training and match management in one place.

Bluecherry is a Linux-focused video surveillance server application designed to manage and record network (IP) cameras. It provides web-based configuration and live viewing alongside desktop and mobile clients for cross-platform access. The project transitioned to community-driven, GPL-licensed development and supports deployment via native packages or Docker.

Key Features

• ONVIF-compatible IP camera support for wide device interoperability
• Web-based configuration and live viewing with role-based user access
• Cross-platform clients for Linux, Windows, macOS and mobile (iOS/Android)
• Low memory footprint and CPU-efficient design suitable for modest servers
• Unlimited camera counts with server-side recording and playback capabilities
• Email and webhook notifications for events and alerts
• Docker-based installation option and virtual machine friendly deployment
• Native build system with bundled libraries (libav/ffmpeg) for video handling

Use Cases

• Small business or retail multi-camera NVR deployment with centralized recording
• Home surveillance with mobile live view and push/notification integrations
• Edge or VM deployments on resource-constrained servers requiring efficient video recording

Limitations and Considerations

• The server component is primarily targeted at Linux; Windows/macOS act as clients only
• Building from source requires several native dependencies and custom build scripts; Docker install is recommended for simpler deployment
• Advanced codec hardware acceleration and analytics integrations may require additional configuration or supported drivers

Bluecherry provides a full-featured DVR solution focused on IP camera management, efficient resource use, and multi-platform client access. Community-driven development and Docker deployment options make it suitable for varied self-hosted surveillance scenarios.

WhoRang is a self-hosted, AI-powered doorbell intelligence platform that analyzes doorbell camera events to identify visitors, detect scenes, and provide usage analytics. It integrates multiple AI providers, supports local model inference, and provides a mobile-first dashboard with real-time notifications.

Key Features

• Multi-provider AI integration including support for local Ollama models and cloud providers for vision and analysis
• Face recognition and recurring visitor tracking with configurable detection settings
• Real-time WebSocket notifications and live event monitoring
• Mobile-first, responsive dashboard with charts, usage analytics, and exportable reports (CSV/PDF)
• AI usage and cost tracking across providers with provider comparison metrics
• Docker-based deployment with a Node.js/Express backend and a React + Vite frontend

Use Cases

• Home automation: integrate doorbell intelligence into a smart home to trigger automations or alerts
• Security monitoring: analyze visitor patterns, identify repeat visitors, and review event history
• Small business: track customer visits and generate periodic analytics reports

Limitations and Considerations

• Cloud AI providers incur usage costs; local privacy-preserving inference requires setting up a local model host such as Ollama
• The default SQLite database is suitable for single-node or small deployments but may limit scalability for high-volume installations
• Face recognition accuracy and detection reliability depend on camera quality, lighting, and selected AI provider

WhoRang is suited for privacy-conscious users who want local control over doorbell analytics and AI selection. It provides an extensible platform for integrating AI-driven vision into smart-home workflows while exposing trade-offs between convenience, cost, and scalability.

Shinobi is an open-source video surveillance (CCTV) and NVR platform for managing IP cameras, live streams, and recordings from a browser-based interface. It is built for performance and can be deployed on anything from an edge device to an enterprise server.

Key Features

• Live viewing and recording for IP cameras (commonly via RTSP and other FFmpeg-supported inputs)
• Web-based management UI for configuring monitors, recording modes, and retention
• Event-driven, real-time updates to connected clients via WebSockets
• Flexible architecture intended for customization and building additional functionality on top
• Designed around FFmpeg for efficient ingest, recording, and transcoding pipelines

Use Cases

• Home and small business NVR/DVR for multiple IP cameras
• Baby monitor or home monitoring with browser-based viewing
• Construction site or store surveillance with centralized recording and playback

Limitations and Considerations

• Feature set and reliability can depend heavily on camera stream quality and FFmpeg compatibility
• Larger deployments may require careful tuning of storage, retention, and transcoding/recording settings

Shinobi is a practical choice for users who want a self-hosted, web-managed NVR focused on performance and flexibility. It suits both simple home setups and larger multi-camera deployments when properly tuned.