I came across OpenALPR a little while ago when thinking about the general problem of enhancing the value of video feeds. It has an easy to use Python binding so it didn’t take very long to create an rt-ai Edge stream processing element (SPE). Actually, the OpenALPR part of it is one line of code – it takes a jpeg from the video stream and adds any recognized plate info as metadata to the output message. The trivial stream processing network in the screen capture above shows its operation as an inline semantic enhancer of a video stream. The OpenALPR SPE only outputs a video frame it if either already contains metadata or else the OpenALPR SPE has added metadata. In this way, multiple recognizers can be applied to the same frame using a pipeline of SPEs.
While I can now see a few private houses starting to sprout specialized license plate reading cameras (which are optimized for this purpose, especially for night operation), I don’t have anything set up as yet so I had to make do with printing car images and waving them in front of a webcam. Seemed to work fine but it would be nice to have a proper setup.
Recognized license plate metadata then becomes another feature that can be used for machine learning and inference within the edge environment – another step on the path to sentient spaces perhaps.