p_windows_size.width, p_windows_size.height,
p_current_scale).copyTo(patch_feats.scale(0));
DEBUG_PRINT(patch_feats);
- fft.forward_window(patch_feats, p_xf, temp);
- DEBUG_PRINTM(p_xf);
- p_model_xf = p_model_xf * (1. - interp_factor) + p_xf * interp_factor;
- DEBUG_PRINTM(p_model_xf);
+ fft.forward_window(patch_feats, model->xf, temp);
+ DEBUG_PRINTM(model->xf);
+ model->model_xf = model->model_xf * (1. - interp_factor) + model->xf * interp_factor;
+ DEBUG_PRINTM(model->model_xf);
if (m_use_linearkernel) {
- ComplexMat xfconj = p_xf.conj();
- p_model_alphaf_num = xfconj.mul(p_yf);
- p_model_alphaf_den = (p_xf * xfconj);
+ ComplexMat xfconj = model->xf.conj();
+ model->model_alphaf_num = xfconj.mul(model->yf);
+ model->model_alphaf_den = (model->xf * xfconj);
} else {
// Kernel Ridge Regression, calculate alphas (in Fourier domain)
cv::Size sz(Fft::freq_size(feature_size));
ComplexMat kf(sz.height, sz.width, 1);
- (*gaussian_correlation)(kf, p_model_xf, p_model_xf, p_kernel_sigma, true, *this);
+ (*gaussian_correlation)(kf, model->model_xf, model->model_xf, p_kernel_sigma, true, *this);
DEBUG_PRINTM(kf);
- p_model_alphaf_num = p_yf * kf;
- p_model_alphaf_den = kf * (kf + p_lambda);
+ model->model_alphaf_num = model->yf * kf;
+ model->model_alphaf_den = kf * (kf + p_lambda);
}
- p_model_alphaf = p_model_alphaf_num / p_model_alphaf_den;
- DEBUG_PRINTM(p_model_alphaf);
+ model->model_alphaf = model->model_alphaf_num / model->model_alphaf_den;
+ DEBUG_PRINTM(model->model_alphaf);
// p_model_alphaf = p_yf / (kf + p_lambda); //equation for fast training
}
}
#endif
- cv::Size csz = Fft::freq_size(feature_size);
- p_model_xf.create(csz.height, csz.width, p_num_of_feats);
- p_yf.create(csz.height, csz.width, 1);
- p_xf.create(csz.height, csz.width, p_num_of_feats);
+ model.reset(new Model(Fft::freq_size(feature_size), p_num_of_feats));
#ifndef BIG_BATCH
for (auto scale: p_scales)
// window weights, i.e. labels
MatScales gsl(1, feature_size);
gaussian_shaped_labels(p_output_sigma, feature_size.width, feature_size.height).copyTo(gsl.plane(0));
- fft.forward(gsl, p_yf);
- DEBUG_PRINTM(p_yf);
+ fft.forward(gsl, model->yf);
+ DEBUG_PRINTM(model->yf);
// train initial model
train(input_rgb, input_gray, 1.0);
DEBUG_PRINTM(zf);
if (kcf.m_use_linearkernel) {
- kzf = zf.mul(kcf.p_model_alphaf).sum_over_channels();
+ kzf = zf.mul(kcf.model->model_alphaf).sum_over_channels();
} else {
- gaussian_correlation(kzf, zf, kcf.p_model_xf, kcf.p_kernel_sigma, false, kcf);
+ gaussian_correlation(kzf, zf, kcf.model->model_xf, kcf.p_kernel_sigma, false, kcf);
DEBUG_PRINTM(kzf);
- kzf = kzf.mul(kcf.p_model_alphaf);
+ kzf = kzf.mul(kcf.model->model_alphaf);
}
kcf.fft.inverse(kzf, response);
Kcf_Tracker_Private &d;
- //model
- ComplexMat p_yf;
- ComplexMat p_model_alphaf;
- ComplexMat p_model_alphaf_num;
- ComplexMat p_model_alphaf_den;
- ComplexMat p_model_xf;
- ComplexMat p_xf;
+ class Model {
+ uint height, width, n_feats;
+ public:
+ ComplexMat yf {height, width, 1};
+ ComplexMat model_alphaf {height, width, n_feats};
+ ComplexMat model_alphaf_num {height, width, n_feats};
+ ComplexMat model_alphaf_den {height, width, n_feats};
+ ComplexMat model_xf {height, width, n_feats};
+ ComplexMat xf {height, width, n_feats};
+
+ Model(cv::Size freq_size, uint _n_feats) : height(freq_size.height), width(freq_size.width), n_feats(_n_feats) {}
+ };
+
+ std::unique_ptr<Model> model;
class GaussianCorrelation {
public:
MatScales k;
};
+
//helping functions
void scale_track(ThreadCtx &vars, cv::Mat &input_rgb, cv::Mat &input_gray);
cv::Mat get_subwindow(const cv::Mat &input, int cx, int cy, int size_x, int size_y) const;