KCF跟踪算法代码摘要

通过近期对CSK，CN和KCF等跟踪算法的学习，感觉它们是主要流程都是差不多的，大概可以分解为如下几步：

基本框架如下图所示：

接下来介绍KCF中的一些具体的代码：

1.图片特征的提取：KCF中给出了2中特征

    if features.hog,
        %HOG features, from Piotr's Toolbox
        x = double(fhog(single(im) / 255, cell_size, features.hog_orientations));
        x(:,:,end) = [];  %remove all-zeros channel ("truncation feature")
    end

    if features.gray,
        %gray-level (scalar feature)
        x = double(im) / 255;

        x = x - mean(x(:));
    end

2.介绍了3中核函数：gaussian，polynomical和linear

function kf = gaussian_correlation(xf, yf, sigma)
     N = size(xf,1) * size(xf,2);
    xx = xf(:)' * xf(:) / N;  %squared norm of x
    yy = yf(:)' * yf(:) / N;  %squared norm of y

    %cross-correlation term in Fourier domain
    xyf = xf .* conj(yf);
    xy = sum(real(ifft2(xyf)), 3);  %to spatial domain

    %calculate gaussian response for all positions, then go back to the
    %Fourier domain
    kf = fft2(exp(-1 / sigma^2 * max(0, (xx + yy - 2 * xy) / numel(xf))));

end

function kf = polynomial_correlation(xf, yf, a, b)

    xyf = xf .* conj(yf);
    xy = sum(real(ifft2(xyf)), 3);  %to spatial domain

    %calculate polynomial response for all positions, then go back to the
    %Fourier domain
    kf = fft2((xy / numel(xf) + a) .^ b);

end

function kf = linear_correlation(xf, yf)

     kf = sum(xf .* conj(yf), 3) / numel(xf);

end

3.响应的计算和中心坐标的预测

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 = real(ifft2(model_alphaf .* kzf));  %equation for fast detection
[vert_delta, horiz_delta] = find(response == max(response(:)), 1);
if vert_delta > size(zf,1) / 2,  %wrap around to negativ
    vert_delta = vert_delta - size(zf,1);
end
if horiz_delta > size(zf,2) / 2,  
    horiz_delta = horiz_delta - size(zf,2);
end
pos = pos + cell_size * [vert_delta - 1, horiz_delta - 1];