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:
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:
- Creating a custom Haar Classifier.
- Considering shapes.
- Considering outlines.
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?