CODEWITHSUNDEEP
c++opencv
gankoji
gankoji

Reputation: 851

Region of Interest Uniqueness and Identity

I'm currently working a computer vision application with OpenCV. The application involves target identification and characteristic determination. Generally, I'm going to have a target cross into the visible region and slowly move through it in a couple of seconds. This should give me upwards of 50-60 frames from the camera in which I'll be able to find the target.

We have successfully implemented the detection algorithms using SWT and OCR (the targets all have alphanumeric identifiers, which makes them relatively easy to pick out). What I want to do is use as much of the data as possible from all 50-60 shots of each target. To do this, I need some way to identify that a particular ROI of image 2 contains the same target as another ROI from image 1.

What I'm asking for a little advice from anyone who may have come across this before. How can I easily/quickly identify, within a reasonable error margin, that ROI #2 has the same target as ROI#1? My first instinct is something like this:

  1. Detect targets in frame 1.
  2. Calculate certain unique features of each of the targets in frame 1. Save.
  3. Get frame 2.
  4. Immediately look for ROIs which have the same features as those calc'd in step 2. Grab these and send them down the line for further processing, skipping step 5.
  5. Detect new targets in frame 2.
  6. Pass targets to a thread to calculate shape, color, GPS coordinates, etc.
  7. Lather, rinse, repeat.

I'm thinking that SURF or SIFT features might be a way to accomplish this, but I'm concerned that they might have trouble identifying targets as the same from frame to frame due to distortion or color fade. I don't know how to set a threshold on SIFT/SURF features.

Thank you in advance for any light you can shed on this matter.

Upvotes: 4

Views: 159

Answers (1)

Boyko Perfanov
Boyko Perfanov

Reputation: 3047

One thing you can do is locally equalize brightness and possibly saturation levels. If you aren't using an advanced space such as YCrCb or HSV, I suggest you try them.

Can you assume that the object is not moving too fast? If you feed the previous position in the detection routine, you can decrease the size of the window you are looking at. Same thing goes with the speed, and direction of movement.

I've successfully used histogram composition and shape descriptors of a region in order to reliably detect it, you can use that or add it to a SURF/SIFT classifier.

Upvotes: 2

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