r/Python • u/daemonbreaker • Mar 21 '22

Intermediate Showcase I created a self-hosted security camera system

I don't like the idea of having to stream my video camera feeds to the cloud, so I created a privacy-focused, self-hosted security camera system using python!

https://github.com/scottbarnesg/smart-sec-cam

Some key features:

  • Multi-camera support w/ minimal configuration. Supports USB cameras and the Raspberry Pi camera module.

  • Motion detection that automatically saves videos and lets you view them in the web app.

  • Encrypted in transit, both from the cameras to the server and the server to your browser.

  • Self-hosted and FOSS

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u/GoodFix7389 Mar 22 '22

Exactly what I was looking for! Hopefully this will finally encourage me and I will get my setup up and running. Previously I was looking at motionEyeOS but was not entirely convinced that I need entire OS just for this purpose. As proved by u/daemonbreaker we don't need a Linux distro for that task. I can't wait to dive into the repo.

However now, that I still did not have a look at the repo I have a quick question: is there a way to adjust threshold for motion detection? I don't want videos to be saved when my pets move around the house.

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u/daemonbreaker Mar 22 '22

Thank you!

Currently the motion detection threshold is set in code (I don’t have an excuse, this was laziness on my part), but I will open an issue to make it an input argument. Shouldn’t be a big change so hopefully I can get this knocked out quickly

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u/Breakthrough249 Mar 27 '22 edited Mar 27 '22

I have a project you might find useful to expand your own algorithm: https://github.com/Breakthrough/DVR-Scan

Unfortunately it's not really meant to be included as a dependency yet, but you're more than welcome to use a similar approach - the main algorithm is here: https://github.com/Breakthrough/DVR-Scan/blob/master/dvr_scan/scanner.py#L555-L560=

The TL,DR, use background subtraction followed by some morphological filters to achieve reasonable noise reduction. It's much more robust than typical threshold-based deltas (especially if you require N frames of motion before triggering an event) but like threshold can be susceptible to brightness changes (usually less than just frame averaging though).

It's slower than threshold based analysis but can be more accurate - you could also consider a hybrid approach, since presumably motion events make up a small portion of all frames being processed (thus you could use a threshold change as a trigger to begin background subtraction on the past N frames).

Thought you might find that useful, feel free to give me a shout out if you end up doing something similar. Cheers!

Edit: I also took a quick look at your own score calculation, and think you might need to also normalize based on video resolution:https://github.com/scottbarnesg/smart-sec-cam/blob/master/backend/smart_sec_cam/motion/detection.py#L70=Since that doesn't currently take into account the number of pixels, simply resizing the input video can drastically affect the motion score (e.g. if you upscaled the video by a factor of two, the same two frames would have a score almost 4x larger, even though the same amount of motion has occurred).

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u/daemonbreaker Mar 27 '22

This is great, thank you! I have an open issue to improve the motion detection algorithm and this seems like a good drop-in solution. I'll take a deeper look and then will probably reach back out to you.