Cascade detectors have been shown to operate extremely rapidly, with high accuracy, and have important applications in different spheres. The initial goal for this cascade implementation was the fast and accurate pedestrian detector but it also useful in general. Soft cascade is trained with AdaBoost. But instead of training sequence of stages, the soft cascade is trained as a one long stage of T weak classifiers. Soft cascade is formulated as follows:
where :math:`\texttt{s}_t(x) = \alpha_t\texttt{h}_t(x)` are the set of thresholded weak classifiers selected during AdaBoost training scaled by the associated weights. Let
be the partial sum of sample responses before :math:`t`-the weak classifier will be applied. The funtcion :math:`\texttt{H}_t(x)` of :math:`t` for sample :math:`x` named *sample trace*.
After each weak classifier evaluation, the sample trace at the point :math:`t` is compared with the rejection threshold :math:`r_t`. The sequence of :math:`r_t` named *rejection trace*.
The sample has been rejected if it fall rejection threshold. So stageless cascade allows to reject not-object sample as soon as possible. Another meaning of the sample trace is a confidence with that sample recognized as desired object. At each :math:`t` that confidence depend on all previous weak classifier. This feature of soft cascade is resulted in more accurate detection. The original formulation of soft cascade can be found in [BJ05]_.
gpu::SCascade
-----------------------------------------------
.. ocv:class:: gpu::SCascade : public Algorithm
Implementation of soft (stageless) cascaded detector. ::
class CV_EXPORTS SCascade : public Algorithm
{
struct CV_EXPORTS Detection
{
ushort x;
ushort y;
ushort w;
ushort h;
float confidence;
int kind;
enum {PEDESTRIAN = 0};
};
SCascade(const double minScale = 0.4, const double maxScale = 5., const int scales = 55, const int rejfactor = 1);
:param image: a frame on which detector will be applied.
:param rois: a regions of interests mask generated by genRoi. Only the objects that fall into one of the regions will be returned.
:param objects: an output array of Detections represented as GpuMat of detections (SCascade::Detection). The first element of the matrix is actually a count of detections.
:param stream: a high-level CUDA stream abstraction used for asynchronous execution.
:param image: a frame on which detector will be applied.
:param rois: a regions of interests mask generated by genRoi. Only the objects that fall into one of the regions will be returned.
:param objects: an output array of Detections represented as GpuMat of detections (SCascade::Detection). The first element of the matrix is actually a count of detections.
:param stream: a high-level CUDA stream abstraction used for asynchronous execution.
