Evaluate specified ROI and return confidence value for each location in multiple scales
[rcts, locations] = hog.detectMultiScaleROI(im, locations)
[...] = hog.detectMultiScaleROI(..., 'OptionName',optionValue, ...)
Input
- im 8-bit 1- or 3-channel image where objects are detected.
- locations input detection region of interest. It specifies
candidate locations to search for object detections at
different scales. An struct array with the following fields:
- scale scale (size) of the bounding box, scalar.
- locations set of requested locations to be evaluated,
cell array of points
{[x,y], ...}.
- confidences vector that will contain confidence values
for each location. Not required on input, this will be
filled/updated on output.
Output
- rcts Detected objects boundaries. Cell array of rectangles
where objects are found, of the form
{[x,y,w,h], ...}.
- locations output updated
locations struct array. All
points are retained, but their confidences are updated.
Options
- HitThreshold Threshold for the distance between features
and SVM classifying plane. Usually it is 0 and should be
specified in the detector coefficients (as the last free
coefficient). But if the free coefficient is omitted (which is
allowed), you can specify it manually here. default 0
- GroupThreshold Minimum possible number of rectangles in a
group minus 1. The threshold is used on a group of rectangles
to decide whether to retain it or not. If less than or equal
to zero, no grouping is performed. See cv.groupRectangles.
default 0