Moonfire NVR, a security camera network video recorder
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Scott Lamb 1c904b925a many improvements to install docs/procedures
* in markdown files, use code fences rather than indented blocks.
    This is harder to screw up (one of them was off by a space so didn't
    render properly) and allows me to add info strings.

  * uniformly use "useradd" to create the user and group in all three
    places (install-manual.md, script-functions.sh, Dockerfile) rather
    than addgroup + adduser. Create a full home dir, which I suspect was
    the problem in #67. Don't allow customizing group name; it's always
    the same as the user.

  * install the sqlite3 package so that the "moonfire-nvr sql" command
    works properly.

  * remove "setup_db" function, which was out of place. Since the
    creation of the "moonfire-nvr init" command, this has to happen
    after installation of the binary. install.md gives instructions on
    this part anyway so remove it from the script.

  * give a proper command to create the db dir. It was creating it
    within the current directory, not within /var/lib/moonfire-nvr.
    Don't bother creating sample directory; "moonfire-nvr config"
    will do this.

  * when setting owners on a newly created directory, use a single
    "install -d" command rather than "mkdir" + "chown".

  * address confusion about whether sample file dirs need to be
    precreated. (Only when Moonfire NVR doesn't have write permissions
    on the parent.)

  * always just install the packaged version of ffmpeg rather than
    building our own. This has been usable since Debian/Raspbian 9
    Stretch; Debian/Raspbian 10 Buster is out now so there's no excuse
    for still running Debian/Raspbian 8 Jessie.

  * don't chown the UI directory; it can be owned by root as with
    the binary.

  * in scripts/install.sh, don't enable/start the service yet. It hasn't
    been configured.
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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.

screenshot

This is version 0.1, the initial release. 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 3 TB drives costing about $100), so we can afford to keep many camera-months of both streams on disk.
  • decoding and analyzing only select "key" video frames (see wikipedia).
  • off-loading expensive work to a GPU. Even the Raspberry Pi has a surprisingly powerful GPU.
  • using HTTP Live Streaming rather than requiring custom browser plug-ins.
  • taking advantage of cameras' built-in motion detection. This is the most obvious way to reduce motion detection CPU. It's a last resort because these cheap cameras' proprietary algorithms are awful compared to those described on changedetection.net. Cameras have high false-positive and false-negative rates, are hard to experiment with (as opposed to rerunning against saved video files), and don't provide any information beyond if motion exceeded the threshold or not.

Documentation

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.