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moonfire-nvr/README.md

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Introduction

Moonfire NVR is an open-source security camera network video recorder, started by Scott Lamb <slamb@slamb.org>. It saves H.264-over-RTSP streams from IP cameras to disk into a hybrid format: video frames in a directory on spinning disk, other data in a SQLite3 database on flash. It can construct .mp4 files for arbitrary time ranges on-the-fly. It does not decode, analyze, or re-encode video frames, so it requires little CPU. It handles six 1080p/30fps streams on a Raspberry Pi 2, using less than 10% of the machine's total CPU.

Help wanted to make it great! Please see the contributing guide.

So far, the web interface is basic: a filterable list of video segments, with support for trimming them to arbitrary time ranges. No scrub bar yet. There's also an experimental live view UI.

list view screenshot live view screenshot

There's no support yet for motion detection, no https/TLS support (you'll need a proxy server, as described here), and only a console-based (rather than web-based) configuration UI.

Moonfire NVR is currently at version 0.7.3. Until version 1.0, there will be no compatibility guarantees: configuration and storage formats may change from version to version. There is an upgrade procedure but it is not for the faint of heart.

I hope to add features such as video analytics. In time, we can build a full-featured hobbyist-oriented multi-camera NVR that requires nothing but a cheap machine with a big hard drive. There are many exciting techniques we could use to make this possible:

  • avoiding CPU-intensive H.264 encoding in favor of simply continuing to use the camera's already-encoded video streams. Cheap IP cameras these days provide pre-encoded H.264 streams in both "main" (full-sized) and "sub" (lower resolution, compression quality, and/or frame rate) varieties. The "sub" stream is more suitable for fast computer vision work as well as remote/mobile streaming. Disk space these days is quite cheap (with 4 TB drives costing about $100), so we can afford to keep many camera-months of both streams on disk.
  • off-loading on-NVR analytics to an inexpensive USB or M.2 neural network accelerator and hardware H.264 decoders.
  • taking advantage of on-camera analytics. They're often not as accurate, but they're the best way to stretch very inexpensive NVR machines.

Documentation