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I also enforced some invariants in the signals code, fixing a couple bugs. The signals code is more complex than I'd like, but hopefully is working now. |
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| docker | ||
| guide | ||
| misc | ||
| server | ||
| ui | ||
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| CHANGELOG.md | ||
| LICENSE.txt | ||
| README.md | ||
| release.bash | ||
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README.md
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.
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 no support for motion detection, no https/SSL/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 0.6.2. 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 salient motion detection. It's way too early to make promises, but it seems possible to build a full-featured hobbyist-oriented multi-camera NVR that requires nothing but a cheap machine with a big hard drive. I welcome help; see Getting help and getting involved below. 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.
- using HTTP Live Streaming rather than requiring custom browser plug-ins.
- taking advantage of on-camera analytics. This is the lowest CPU usage option, although many cameras' analytics aren't as good as what can be done on the NVR, they're hard to experiment with, and even when they use modern ML-based approaches, their built-in models can't be retrained.
Documentation
- License — GPL-3.0-or-later with https://spdx.org/licenses/GPL-3.0-linking-exception.html for OpenSSL.
- Installing
- Building from source
- UI Development
- Troubleshooting
- Wiki has notes on several camera models. Please add yours!
Getting help and getting involved
Please email the moonfire-nvr-users mailing list with questions, or just to say you love/hate the software and why. You can also file bugs and feature requests on the github issue tracker.
I'd welcome help with testing, development (in Rust, JavaScript, and HTML), user interface/graphic design, and documentation. Please email the mailing list if interested. Pull requests are welcome, but I encourage you to discuss large changes on the mailing list or in a github issue first to save effort.