I wanted to analyze the in-built object detector in opencv (CascadeClassifier, using Haar or Hog features). Is it possible to control the false positive or hit rate of the classifier? I would ideally like to plot an ROC or DET curve of the same.
Hi, in case of HoG you can vary the hit_threshold and group_threshold parameters of the detectMultiScale method to control the number of false positives and true positives.
From the opencv documentation gpu::HOGDescriptor::detectMultiScale (the CPU HoG documentation is currently missing, its a bug in the OpenCV documentation. But the GPU HoG has a quite similar interface so we can look a this one).
hit_threshold - Threshold for the distance between features and SVM classifying plane
group_threshold - Coefficient to regulate the similarity threshold. When detected, some objects can be covered by many rectangles. 0 means not to perform grouping. See groupRectangles())
In my project I am using MeanShiftGrouping=true. MeanShiftGrouping combines the detected rectangles and weights of the detected objects into one result. So we get an resulting rectangle and weight. This weight can be "threshold-ed" by using group_threshold. For the DET diagram in my project I am using therefore a small value for hit_threshold and vary then the group_threshold.
If you use the DET or ROC plot to find the "best" parameter setting for your application you should vary both, the hit_threshold and the group_threshold.
Hi, take a look at the paper from Viola and Jones http://research.microsoft.com/~viola/Pubs/Detect/violaJones_IJCV.pdf at figure 6 on page 12. The detection cascade looks like an unbalanced decision tree. At every layer a classifier is used to reject wrong hypotheses. Probably you can use the search depth in the decision tree as a kind of "weight". The CascadeClassifier::runAt() method provides this information (see http://docs.opencv.org/modules/objdetect/doc/cascade_classification.html?highlight=haar#cascadeclassifier-runat)
Asked: 2013-01-22 01:06:17 -0600
Seen: 1,287 times
Last updated: Jan 22 '13
