On Building an Instagram Street Art Dataset and Detection Model
blog.floydhub.com
blog.floydhub.com
There is a trope around graffiti artists that their biggest fans are the police. Often an artist will spend all night painting a mural, and then by the morning its already gone, but the paint "buffers" submit a photo to the police.
I definitely think there will be a cat-and-mouse synonymous with the adversarial neural networks examples for hiding weapons in everyday objects.
At the end of the day, most street art is commissioned or done in pre-approved areas, so I don't think it will be a cause of concern with larger artists.
Literally every single tube line/carriage/station has his tag, tox followed by the year, eg tox 16, repeated over and over again
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I wonder if you could use their provided StreetView to generate a bunch of perspective views for the same artwork... or if computing a view from another angle could be a way to fuzz your existing training set to generate more examples.
I want to try and map out street art in cities by using the landmark points in the background of images. Theres a few neat datasets that do landmark -> geodata translation, but as expected, its limited.
That being said, I can imagine in a very near future, it will be possible to train against google maps and get enough "landmarks" to geolocalize images.
This is straight up surveillance that will cannibalize what it's observing.
They're quite creative, they're gonna dazzle your machine vision. Why build tools to hinder/distort/build paranoia into something you're interested in if you genuinely are?
Instagram has a really terrible web UI as well. When I want to share an image I see on it I have to open the inspector to get the image url because they blocked right click copy image.
Then it is a simple image classifier with one dimension (graffit, not graffit).