u/mitbull420

On my morning walk this morning in Vic Park I saw an office worker across the road scream at a homeless person to "get a fucking job you fucking homeless cunt" when the homeless person approached them.

The homeless person wasn't aggressive at all and was probably just asking for change or mumbling something.

I'm so shocked that there are people out there like this. I know this advice doesn't apply to most people here but please be nice to homeless people. They're a really vulnerable population and don't deserve your abuse.

https://preview.redd.it/llqlupnwng4h1.png?width=2188&format=png&auto=webp&s=7fae5860babaffa1c8bfdcb1468b374eb38ac55d

I'm currently building a computer vision application. I've managed to successfully train a YOLO model to detect the object I'm interested in for my videos.

The image above shows some visualisations of the YOLO model outputs for some of the videos. I want to essentially cluster these strands in the image into groups based on their separation distance and return a string telling me the number of strands in each group from left to right (e.g. 1-2-3).

The target value for each column in the image (where each column corresponds to a video) is 1-2-3, 1-2-3-2-3, 1-1-2-3-3-3-3 and don't worry about the fourth column for now 😄.

The rows show the x vs t, y vs t and x vs y vs t for all the detections and the points are sized based on the detection box area.

In the fourth column I have some background object detections which I want to ignore hence why I've also visualised detection box area.

I've managed to train a XGBoost classification model that gives 70ish% accuracy however Bayes error is making me think I should be able to do much better than this.

How would you approach trying to predict these strand clusterings?

Some extra info that might help; there are at max 8 groups and each group can have only at max 3 strands.