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algorithmimage-processingopenalpr

Algorithm or tool for finding edges of a different area when comparing two images

I am working on a community project which goal is to reduce the speeding violations. In order to recognize the car's license plates I am using OpenALPR. The problem is that it is sensitive to camera position, that is the angle and OpenALPR have troubles detecting the LP when the angle is greater than 20 degrees (yes, I've read the recommendations about the good camera position but IRL sometimes they cannot be satisfied).

I found that the problem is that the LP area is not detected. However, cropping manually the image to contain just the car without any other modifications of the pixels (like filtering) fixes the problem and OpenALPR is able to detect the LP area.

I am looking for a solution that can do the cropping automatically. Either algorithm or tool that can compare two images "base" and "target" and return the coordinates (top left, bottom right) of the changed area in the target image.

Alternative solution would be different configuration file for the OpenALPR. I am experimenting with this last few hours but with no success.

Base image will look like: enter image description here

Target image will look like: enter image description here

(these are just two frames from a video)

(original image size is much bigger, i.e. 3840x2160)

Are there algorithm(s) or tools that can help me with automating this task?

Solution

  • The basic method is by differencing, i.e. taking the absolute difference of the RGB component values pixel per pixel. Where differences are large, there is a detection.

    But this can work poorly (and it does with the given images) because the two pictures may be slightly unaligned, and wind can move the vegetation.

    So I recommend to

    • reduce the image resolution by a significant factor (say 8);

    • blur the reduced images;

    • compute the absolute differences;

    • keep the largest differences among the components;

    • binarize with a threshold;

    • finally use connected components labelling to find the most significant blob and eliminate the residual interferences.

    enter image description here

    Make sure to refresh the background image (when you are sure there is no car) to avoid the effect of daily drift (there are always slow changes). It may also be useful to normalize the image intensity to thwart the changes is ambiant lighting (passing clouds f.i.).