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74 lines
3.8 KiB
Markdown
74 lines
3.8 KiB
Markdown
[](https://github.com/scottlamb/moonfire-nvr/actions?query=workflow%3ACI)
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# Introduction
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Moonfire NVR is an open-source security camera network video recorder, started
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by Scott Lamb <<slamb@slamb.org>>. It saves H.264-over-RTSP streams from
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IP cameras to disk into a hybrid format: video frames in a directory on
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spinning disk, other data in a SQLite3 database on flash. It can construct
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`.mp4` files for arbitrary time ranges on-the-fly. It does not decode,
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analyze, or re-encode video frames, so it requires little CPU. It handles six
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1080p/30fps streams on a [Raspberry Pi
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2](https://www.raspberrypi.org/products/raspberry-pi-2-model-b/), using
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less than 10% of the machine's total CPU.
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So far, the web interface is basic: a filterable list of video segments,
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with support for trimming them to arbitrary time ranges. No scrub bar yet.
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There's also no support for motion detection, no https/SSL/TLS support (you'll
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need a proxy server, as described [here](guide/secure.md)), and only a
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console-based (rather than web-based) configuration UI.
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Moonfire NVR is currently at version 0.6. Until version 1.0, there will be no
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compatibility guarantees: configuration and storage formats may change from
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version to version. There is an [upgrade procedure](guide/schema.md) but it is
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not for the faint of heart.
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I hope to add features such as salient motion detection. It's way too early to
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make promises, but it seems possible to build a full-featured
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hobbyist-oriented multi-camera NVR that requires nothing but a cheap machine
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with a big hard drive. I welcome help; see [Getting help and getting
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involved](#help) below. There are many exciting techniques we could use to
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make this possible:
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* avoiding CPU-intensive H.264 encoding in favor of simply continuing to use the
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camera's already-encoded video streams. Cheap IP cameras these days provide
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pre-encoded H.264 streams in both "main" (full-sized) and "sub" (lower
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resolution, compression quality, and/or frame rate) varieties. The "sub"
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stream is more suitable for fast computer vision work as well as
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remote/mobile streaming. Disk space these days is quite cheap (with 4 TB
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drives costing about $100), so we can afford to keep many camera-months of
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both streams on disk.
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* off-loading on-NVR analytics to an inexpensive USB or M.2 neural network
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accelerator.
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* using [HTTP Live Streaming](https://en.wikipedia.org/wiki/HTTP_Live_Streaming)
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rather than requiring custom browser plug-ins.
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* taking advantage of on-camera analytics. This is the lowest CPU usage option,
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although many cameras' analytics aren't as good as what can be done on the NVR,
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they're hard to experiment with, and even when they use modern ML-based
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approaches, their built-in models can't be retrained.
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# Documentation
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* [License](LICENSE.txt) — GPLv3
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* [Installing](guide/install.md)
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* [Building from source](guide/build.md)
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* [UI Development](guide/developing-ui.md)
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* [Troubleshooting](guide/troubleshooting.md)
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* [Wiki](https://github.com/scottlamb/moonfire-nvr/wiki) has notes on
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several camera models. Please add yours!
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# <a name="help"></a> Getting help and getting involved
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Please email the
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[moonfire-nvr-users](https://groups.google.com/d/forum/moonfire-nvr-users)
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mailing list with questions, or just to say you love/hate the software and
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why. You can also file bugs and feature requests on the
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[github issue tracker](https://github.com/scottlamb/moonfire-nvr/issues).
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I'd welcome help with testing, development (in Rust, JavaScript, and HTML),
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user interface/graphic design, and documentation. Please email the mailing
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list if interested. Pull requests are welcome, but I encourage you to discuss
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large changes on the mailing list or in a github issue first to save effort.
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