For a project I am working on I'd like to be able to detect unique icons/barcodes in video footage. Suppose you have 10 people in the frame where each person is wearing a t-shirt with a similar but unique identifier (something like a barcode) and the programme is to detect each unique identifier.
Here are three options I'm working with now and associated drawbacks:
- Using brute force feature matching (SURF, SIFT or ORB) - not sure how accurate this would be in real-world scenarios as people move and rotate so to if the identifiers are very similar can this still achieve good accuracy?
- Training tiny YOLO model. Now this seems like a good option but is not very scalable and it just seems like there's a simpler solution
- Simple template matching - is not a very good option due to changing perspective, size and angles
Any advise would be highly appreciated!
I'd like to add some images for context. The idea is that each person would have a logo with a unique binary patter around it. See images below:
As you can see the two are similar but not the same due to the stripes around it.
I'm applying an Adoptive Gaussian Blur to it and getting 'barcode' like images that are clearly different
Now, it doesn't seem to me that normal SURF matching would be able to recognise the subtle difference in two images at least not reliably