I'm currently trying to learn about computer vision stuff and I want to try it on a simple project of real time tracking of figures of players in a foosball game while the camera can be moving a little bit above the table. The first goal I want to achieve is detecting (and tracking) the bars on which the figures are attached (so then I can easily find the figures on them).

My idea was to use Hough transform to find the lines that fit to the bars, but I'm wondering whether it couldn't be done using a neural network. Would it have better performance? If so, how could it be done, what approach can be used here? So I could give images of the table from different angles as inputs to the NN and it'd give me 8 lines that fit to the bars. Using the usual objects detection algorithms probably isn't the right way as this seems to be a simpler problem (finding just bars/lines). If you could just point me to some direction or show me some papers or tutorials that solve a similar problem, I'd be grateful.

Note: To train the NN a was thinking about the mentioned Hough transform to find the lines in the training examples.

  • $\begingroup$ I don't think your question is focused enough. Cann NNs be used? Almost certainly yes. Can we explain to you all rou need to know to make it work? No, not in this format -- that's a textbook! Community votes, please: too broad? $\endgroup$
    – Raphael
    Sep 23, 2017 at 13:00
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    $\begingroup$ If you are going to use Hough Transform but hide it in some form of NN, what more could be said? Oh, maybe this, do not use Hough Transform, use Short Term Hough Transform instead, otherwise you will have huge problems. De hoc satis. $\endgroup$
    – Evil
    Sep 23, 2017 at 13:40
  • $\begingroup$ The way to find out whether it will work is to try it and see. That tends to be the answer for most questions of the form "will my machine learning idea work?". I don't see why you aren't using the Hough transform; if you know what you're looking for, and standard methods work, it's not clear why you'd try something different (except for curiousity). $\endgroup$
    – D.W.
    Sep 24, 2017 at 2:10


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