Finding the perfect algorithm for 2dFeatures real time feature detection/matching

asked 2019-02-13 09:45:41 -0600

joinity
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Hello OpenCV Community, I am relatively new to the world of opencv and try to do a project with following requirements:

My robot has to detect a picture in his fixed field of view out of a local database of 200 pictures The camera angle is in a 45° angle and has a fixed height. The images are all presented at a fixed distance but can be presented in a different rotation. The hardware resources are extremely limited and i would like to run it on a raspberry pi in real time at around 5 fps The goal would be to get even cheaper hardware like an Ingenic Chip or an Allwinner one.

My question therefore would be, if there is a perfectly lightweight and 100% failsafe algorithm for this!

I thank you for your help

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if there is a perfectly lightweight and 100% failsafe algorithm for this

lol, silver bullets, dream on.

berak ( 2019-02-13 09:57:58 -0600 )edit

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answered 2019-02-13 09:51:50 -0600

berak
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updated 2019-02-13 12:54:37 -0600

wait:

"out of a local database of 200 pictures" --

are you trying to do some arbitrary "object detection" like this ?

then you're most probably on the wrong bus.

(it won't ever work ! feature matching is for finding parts of a single, known scene only, it can never tell you, if it's NOT there, no matter, which features you choose, it's a constraint of the matching algorithms. it's main application is finding a homography between a part of the scene and the whole, NOT detection.)

are you also aware, that for 200 db images you would need 200 detectAndCompare() calls & matching ?

forget that idea immediately, please, it's a waste of time. instead do some research on depp learning, SSD nets, YOLO, etc. those can be re-trained with quite sparse data on your special problem set, and you can run the inference later from opencv again.

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First of all thanks for your answer! Could you explain how this Video did this then? https://www.youtube.com/watch?v=kbYDj... He said he uses ORB with a databse of 20.000 Images. For SSD nets and YOLO can i train it to detect various magic cards like in the video, with invariant rotation?

joinity ( 2019-02-14 02:51:44 -0600 )edit

sorry i never look at videos (waste of time)

you CAN use 2d features for CBIR like tasks, but that needs extensive (BoW) clustering, and a different type of matching (like a flann index, or SVM classification). it also won't work for detection (or say, localization).

berak ( 2019-02-14 02:56:47 -0600 )edit

Im sorry, i think my language barrier made you think that i want to detect the position. I just want to show the camera a printed card and it detects that i show card x and i have 200 cards.

joinity ( 2019-02-14 03:04:20 -0600 )edit

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Finding the perfect algorithm for 2dFeatures real time feature detection/matching

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