I'm currently working on a project to explore various means of human detection. To formulate a base-line for comparison, I am looking to utilize several standard detection methods (ie, HOG with SVM, Viola-Jones). As I am using the Caltech Pedestrian Dataset(http://www.vision.caltech.edu/Image_Datasets/CaltechPedestrians/) for testing, I am doing all my work in Matlab to take advantage of their additional toolboxes.
I'm currently stuck on the Viola-Jones problem. The detector evaluation tool requires [frame, x, y, h, w, score] to generate ROC curves for the detectors. I had planned to use the CascadeObjectDetector with the OpenCV trained fullbody.xml model with the following code:
detector = vision.CascadeObjectDetector(MODEL)
BBOX = step(detector,I)
However, the detector only gives the Bounding Box values and no score. Is there some work around to generate ROC curves for Cascade Classifiers? Or another implementation that can provide the desired results?
Unfortunately, there is currently no way to get a confidence score from vision.CascadeObjectDetector.