A Data Association Nightmare: Algorithmic Approaches to Tracking my Red Solo Cup at a Party

The Chaz1

1College Freshmen, Cranberry-Lemon University, Pittsburgh, PA, USA, Major Undecided

Abstract

Dude, I’m at this kegger and no one remembered to bring a sharpie. I’ll set down my drink next to someone else’s, and I’ve got no idea which one’s mine. This other dude came back and he didn’t know either. Dude! Okay, so, I realized this is a data association issue, and we have to track these cups better. They look identical except for mine which I spike with some Jim Bean because I’m fancy. Which I guess still looks like everyone else’s cup. Crap! I can keep track of where my drink is, but someone inevitably puts their cup next to mine or moves it because they think it’s theirs or someone needs one some for-beer pong and is checking for an empty. We checked all the drawers in the kitchen twice and we can’t find a sharpie anywhere. Pens don’t work so the only way is to use a better data association algorithm. 

Keywords: Drinking, Data Association, Multiple Solo Cup Tracking, Global Nearest Neighbor, Joint Probability Data Association, Mahalanobis, Cup Distribution, Cup Sensing, Semi-Greedy Algorithm, Multiple Hypothesis Tracker

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Published by B McGraw

B McGraw has lived a long and successful professional life as a software developer and researcher. After completing his BS in spaghetti coding at the department of the dark arts at Cranberry Lemon in 2005 he wasted no time in getting a masters in debugging by print statement in 2008 and obtaining his PhD with research in screwing up repos on Github in 2014. That's when he could finally get paid. In 2018 B McGraw finally made the big step of defaulting on his student loans and began advancing his career by adding his name on other people's research papers after finding one grammatical mistake in the Peer Review process.

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