LED Blinking Frequency

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C:\fakepath\frame12.jpgI decided not to update my old post because it already has so many comments. Here is the program I wrote to detect blinking LEDs. It works so so when the surrounding is a bit dark and doesn't work at all when its bright out there. I have been given some suggestion to improve the efficiency like pre allocating but I think I need to work on the logic as well. Kindly guide me how can I detect the position and of blinking LED? Camera frame rate is 90fps and blinking frequency is 45Hz and there are more than one LEDs in the frame. Attached are two frames in a bright light condition. here is the logic 1. Setup camera parameters to make it 90fps 2. Quickly capture 30 frames and compute the difference and threshold of difference of the frames 3. Find contour centers in the the threshold image 4. organize contours in a R*tree and check the frequency of contour centers in user defined neighborhood. 5. If the count falls within the frequency and tolerance range. Predict the point to be LED light.

Kindly guide me to modify this code so that it works in bright light conditions and the success rate of detecting LED is high.C:\fakepath\frame11.jpg(/upfiles/14359976621521109.jpg).

As suggested the question seems to be too long. I am trying to get the difference between two frames and threshold the difference, check for contours and then check the frequency of contour center to detect the light. Following function accepts N number of images and does as explained. I need this to work in all light scenario, it is working in low light environment only. Kindly guide me how can I modify the code to make it work in any scenario.

       const static int SENSITIVITY_VALUE = 50;
      const static int BLUR_SIZE = 6;


       void getThresholdImage(vector<Mat>  &framesToProcess, vector<Mat> &thresholdImages, vector<Mat> &differenceImages)
 {
   vector<Mat> grayImage;

for (int i = 0; i < framesToProcess.size(); i++)
{
    Mat tempMatImage, tempGrayImage;

    resize(framesToProcess[i], tempMatImage, Size(600, 800));
    cvtColor(tempMatImage, tempGrayImage, COLOR_BGR2GRAY);
    grayImage.push_back(tempGrayImage);

    if (i > 0)
    {
        Mat tempDifferenceImage, tempThresholdImage;
        absdiff(grayImage[i - 1], grayImage[i], tempDifferenceImage);
        imshow("difference Image", tempDifferenceImage);
        //erode(tempDifferenceImage, tempDifferenceImage, Mat(), Point(-1, -1), 2, BORDER_CONSTANT);
        differenceImages.push_back(tempDifferenceImage);
        threshold(tempDifferenceImage, tempThresholdImage, SENSITIVITY_VALUE, 255, THRESH_BINARY);
        imshow("before blur", tempThresholdImage);
        blur(tempThresholdImage, tempThresholdImage, Size(BLUR_SIZE, BLUR_SIZE));
        imshow("After BlurThreshold Image", tempThresholdImage);
        thresholdImages.push_back(tempThresholdImage);
    }
}
}