How computer vision learns to read the streets

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00:00How computer vision learns to read the streets.

00:03Graffiti is a planned, creative craft, not random paint on walls.

00:08Vision systems compare before and after images to spot fresh tags quickly.

00:13A fresh tag on a station pillar or the side of a train can appear between two commutes,

00:17and by the next day it might be covered or replaced.

00:20That's where modern vision systems help.

00:22At the core of a memorable piece is letter form.

00:25Caps and nozzles change line width and texture, from hairline cuts to fat fills,

00:30so a single can can produce outlines, fills, and soft blends.

00:34Many pieces are planned, yet the best still leave room for improvisation responding to

00:38drips, wind, or the way a seam breaks a letter's spine.

00:42This Budapest graffiti mural depicts Bender, the iconic robot from the animated series Futurama,

00:48reimagined in a bold street art style.

00:51That tension between planning and improvisation is exactly what makes their mark so visually

00:55distinct, and why they're such strong signals for cameras and algorithms later.

01:00At street level, the winning recipe is change detection plus object detection.

01:05New images are aligned to those baselines using structure from motion and orthophoto generation,

01:10so yesterday and today, line up pixel for pixel.

01:13A widely used approach trained on facade imagery reached 88% accuracy when detecting markings on

01:19orthophotos, helped by a faster R-CNN backbone.

01:23And a carefully labeled set of 1,682 markings across 1,022 images.

01:29Two Wiens Indigo Work designed a change detection data set with 6,902 image pairs collected over

01:3529 sessions along a 50 meter test site.

01:39As one research paper summarized, with an accuracy of 87% and a recall of 77%, the results show

01:46that the proposed change detection workflow can effectively indicate newly added graffiti.

01:52Large transport trials have shown that AI-assisted CCTV can generate tens of thousands of real-time alerts

01:58reports over a year at a single station.

02:04Now, we're not to mention, we're just now to mention a lot of pixels that are meant by selecting the

02:07filters, so we've seen that in that area of setting, if we can Power joyful control.

02:10And, here we are experts at every level of focusing on the machine.

02:12So that's what we are experiencing to be the same, the movement ofaltung, this is a specific

02:15source of our telescopic system.

02:17So, when we are looking at the same purpose, we're looking at what we're looking at.

02:19So, you can look at the same ones, you can look at the same amount of sensors that are

02:24in the same surface of a mechanical load, which can be used for this function,