Performance ::: haarcascade15 :::

So now that the limitation of the performance application has been identified what does that mean for the previous haar classifiers we have tested and analyzed this far? Well to put it bluntly they were wrong. The results did not truly reflect the classifiers accuracy. So I took the haarcascade15, since this classifier had the best overall results without taking into account the size and position differences.
 
Hit rate ≈ 57.1%

 
The coding excerpt that handles the size and position difference values and calculates whether the detected area is considered a true positive detection (HIT).
det is a pointer to an ObjectPos struct that contains the values for the detected rectangle.
ref is a pointer to an ObjectPos struct that contains the values for the referenced rectangle that was identified through the ObjectMarker.

                    found = 0;
                    for( j = 0; j < refcount; j++ )
                    {
                        distance = sqrtf( (det[i].x - ref[j].x) * (det[i].x - ref[j].x) + 
                                          (det[i].y - ref[j].y) * (det[i].y - ref[j].y) );
                        if( (distance < ref[j].width * maxPosDiff) &&
                            (det[i].width > ref[j].width / maxSizeDiff) &&
                            (det[i].width < ref[j].width * maxSizeDiff) )
                        {
                            ref[j].found = 1;
                            ref[j].neghbors = MAX( ref[j].neghbors, det[i].neghbors );
                            found = 1;
                        }
                    }

With specifying the values for a size and position difference it was pretty much trial and error to see which values gave the most desirable results. These values seemed to do well, and keep the detections within bounds of the real detections.
 

Performance.exe -data "F:\Thesis_Research\AutomatedDetector\HaarClassifier\haarcascade15\haarcascade15.xml" -info "F:\Thesis_Research\AutomatedDetector\Test_Sets\Positive_Solar_Cavities\positivesoutput.txt" -maxSizeDiff 5 -maxPosDiff 4

 
Below is the more accurate portrayal of the classifiers detection rates, this rate is about 89.5% which is definitely alot better than the 57.1% I was racking my brain over before.

Hit rate ≈ 89.4%

Tags: opencv, performance, testing outcomes, thesis, trials