Underwhelming hardware

I used a RPi 3 B board that has a 1.2 Ghz quadcore ARM processor. For processing a video stream and running the motion detection daemon, it's not really very capable and you would end up with stuck/unusable frames on your stream. One of the things that makes a huge difference is the incoming stream frame rate and resolution. I got the best results with sliding down the incoming frame rate to as low as 10 on the camera that I am using.

B vs B+

The B+'s advantage is more on the I/O side and it really doesn't make much of a difference with processing power when it comes to the video stream. On the other hand, the B is more battery friendly which was a major requirement in my setup owing to the frequent power-cuts associated with Indian summers. Overclocking, too, isn't worth it if you consider the battery drain (as high as 20% faster) compared to any noticeable performance gain.

Backup power

As mentioned above, this was an important requirement. I used a 20000 mAH battery that has passthrough. On the downside, when passthrough triggers, there's a momentary disconnect in power which restarts the camera and the RPi which is undesirable but the small downtime is acceptable.

Network

One of the requirements was failover to a backup network but jumping back to the main network once it's back up. A reverse tunnel to a public IP takes care of ssh and http access and could be easily scripted as well. HTTPS is achieved by setting up a nginx reverse proxy on the public facing system and integrating with letsencrypt.

Motion detection

False positives is a major challenge and I could get a good compromise with a mix of a few things: – Setting up a manual mask. This is easy to do with the motioneye http interface. – Using a despeckle filter. Take a look at this article for a nice write up by the author. After experimenting with several combinations, EedDl gave the best results (which also happens to be the recommended starting poin). – Experimenting with thresholds. I used the threshold_maximum parameter to minimize the maximum pixel change. A script changes the threshold value based on input from a LDR similar to this setup.

Usability

The system is easy to use/configure with the Motioneye http interface but to make it a little bit more interesting, I used some NFC tags to enable/disable motion detection. This can be easily done with Tasker along with the NFC plugin for it. This script takes care of syncing up the config file with the current state of motion detection.

#thoughts #tech #diy #rpi #bash

Discuss...