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What algorithms or approaches apart from Haar cascades could be used for custom objects detection?

asked 2015-04-01 13:04:24 -0600

diegoaguilar gravatar image

updated 2015-04-01 13:18:40 -0600

I need to do computer visions tasks in order to detect watter bottles or soda cans. I will obtain 'frontal' images of bottles, soda cans or any other random objects (one by one) and my algorithm should determine whether it's a bottle, a can or any of them.

Some details about object detecting scenario:

  • As mentioned, I will test one single object per image/video frame.
  • Not all watter bottles are the same. There could be color in plastic, lid or label variation. Maybe some could not get label or lid.
  • Same about variation goes for soda cans. No wrinkled soda cans are gonna be tested though.
  • There could be small size variation between objects.
  • I could have a green (or any custom color) background.
  • I will do any needed filters on image.
  • This will be run on a Raspberry Pi.

Just in case, an example of each:

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I've tested a couple times OpenCV face detection algorithms and I know it works pretty good but I'd need to obtain an special Haar Cascades features XML file for detecting each custom object on this approach.

So, the distinct alternatives I have in mind are:

I'd like to get a simple algorithm and I think creating a custom Haar classifier could be even not needed. What would you suggest?

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answered 2015-04-01 16:46:23 -0600

edgar gravatar image

If you only want to recognize them, then take a look at this tutorial using 2d features:

not sure about the performance on raspi. Also take into account the size of your dataset. The bigger the size, the more complex the problem.

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Asked: 2015-04-01 13:04:24 -0600

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Last updated: Apr 01 '15