SAMF

search_size = [1  0.985 0.99 0.995 1.005 1.01 1.015];

for i=1:size(search_size,2)
                tmp_sz = floor((target_sz * (1 + padding))*search_size(i));
                param0 = [pos(2), pos(1), tmp_sz(2)/window_sz(2), 0,...
                        tmp_sz(1)/window_sz(2)/(window_sz(1)/window_sz(2)),0];
                param0 = affparam2mat(param0); 
                patch = uint8(warpimg(double(im), param0, window_sz));
                zf = fft2(get_features(patch, features, cell_size, cos_window,w2c));

                %calculate response of the classifier at all shifts
                switch kernel.type
                case 'gaussian',
                    kzf = gaussian_correlation(zf, model_xf, kernel.sigma);
                case 'polynomial',
                    kzf = polynomial_correlation(zf, model_xf, kernel.poly_a, kernel.poly_b);
                case 'linear',
                    kzf = linear_correlation(zf, model_xf);
                end
                response(:,:,i) = real(ifft2(model_alphaf .* kzf));  %equation for fast detection
            end
            %target location is at the maximum response. we must take into
            %account the fact that, if the target doesn't move, the peak
            %will appear at the top-left corner, not at the center (this is
            %discussed in the paper). the responses wrap around cyclically.
            [vert_delta,tmp, horiz_delta] = find(response == max(response(:)), 1);