解释一下这段代码function [bg_hist_new, fg_hist_new] = updateHistModel(new_model, patch, bg_area, fg_area, target_sz, norm_area, n_bins, grayscale_sequence, bg_hist, fg_hist, learning_rate_pwp) % Get BG (frame around target_sz) and FG masks (inner portion of target_sz) pad_offset1 = (bg_area-target_sz)/2; % we constrained the difference to be mod2, so we do not have to round here assert(sum(pad_offset1==round(pad_offset1))==2, 'difference between bg_area and target_sz has to be even.'); bg_mask = true(bg_area); % init bg_mask pad_offset1(pad_offset1<=0)=1; bg_mask(pad_offset1(1)+1:end-pad_offset1(1), pad_offset1(2)+1:end-pad_offset1(2)) = false; pad_offset2 = (bg_area-fg_area)/2; % we constrained the difference to be mod2, so we do not have to round here assert(sum(pad_offset2==round(pad_offset2))==2, 'difference between bg_area and fg_area has to be even.'); fg_mask = false(bg_area); % init fg_mask pad_offset2(pad_offset2<=0)=1; fg_mask(pad_offset2(1)+1:end-pad_offset2(1), pad_offset2(2)+1:end-pad_offset2(2)) = true; fg_mask = mexResize(fg_mask, norm_area, 'auto'); bg_mask = mexResize(bg_mask, norm_area, 'auto'); %% (TRAIN) BUILD THE MODEL if new_model % from scratch (frame=1) bg_hist_new = computeHistogram(patch, bg_mask, n_bins, grayscale_sequence); fg_hist_new = computeHistogram(patch, fg_mask, n_bins, grayscale_sequence); else % update the model bg_hist_new = (1 - learning_rate_pwp)*bg_hist + learning_rate_pwp*computeHistogram(patch, bg_mask, n_bins, grayscale_sequence); fg_hist_new = (1 - learning_rate_pwp)*fg_hist + learning_rate_pwp*computeHistogram(patch, fg_mask, n_bins, grayscale_sequence); end end

这段代码实现的是更新一个目标模型的过程。具体来说，它将输入的图像 patch 转化为一个直方图模型（包括背景和前景直方图）并更新这个模型。其中，背景和前景的区域通过 bg_area 和 fg_area 指定，目标区域的大小为 target_sz。在计算直方图时，将图像缩小到 norm_area 大小，并按照灰度级进行分 bin，分 bin 的数量由 n_bins 指定。更新模型时，根据学习率 learning_rate_pwp 对之前的直方图进行加权平均，并加上当前帧的直方图。如果 new_model 为 true，则表示是第一次构建直方图模型，此时直接计算背景和前景的直方图，否则就将当前帧的直方图与之前的直方图进行融合。

相关问题

解释一下这段代码new_hf_num = bsxfun(@times, conj(yf), xtf) / prod(p.cf_response_size); new_hf_den = (conj(xtf) .* xtf) / prod(p.cf_response_size); if frame == 1 % first frame, train with a single image hf_den = new_hf_den; hf_num = new_hf_num; else % subsequent frames, update the model by linear interpolation hf_den = (1 - p.learning_rate_cf) * hf_den + p.learning_rate_cf * new_hf_den; hf_num = (1 - p.learning_rate_cf) * hf_num + p.learning_rate_cf * new_hf_num; %% BG/FG MODEL UPDATE % patch of the target + padding [bg_hist, fg_hist] = updateHistModel(new_pwp_model, im_patch_bg, bg_area, fg_area, target_sz, p.norm_bg_area, p.n_bins, p.grayscale_sequence, bg_hist, fg_hist, p.learning_rate_pwp); end

这段代码是用于跟踪目标的。具体来说，它实现了一个基于视觉识别的目标跟踪算法。其中，new_hf_num和new_hf_den是新的目标模型的分子和分母，它们是通过将当前帧中的图像特征与目标模型进行卷积得到的。在第一帧中，只使用当前帧的图像来训练模型，因此将new_hf_den和new_hf_num分别设置为hf_den和hf_num。在之后的帧中，使用线性插值来更新模型，其中p.learning_rate_cf是一个学习率参数。在模型更新的同时，还会更新背景/前景模型，以更准确地跟踪目标。

解释一下这段代码 [likelihood_map] = getColourMap(im_patch_pwp, bg_hist, fg_hist, p.n_bins, p.grayscale_sequence); % (TODO) in theory it should be at 0.5 (unseen colors shoud have max entropy) likelihood_map(isnan(likelihood_map)) = 0; % each pixel of response_pwp loosely represents the likelihood that % the target (of size norm_target_sz) is centred on it response_pwp = getCenterLikelihood(likelihood_map, p.norm_target_sz);

这段代码的作用是获取一个颜色地图的概率分布图。首先，将输入的图像块（im_patch_pwp）和背景直方图（bg_hist）以及前景直方图（fg_hist）作为输入，根据颜色分布计算像素点的概率值，并将结果存储在likelihood_map中。其中，p.n_bins表示直方图的bin数量，p.grayscale_sequence表示是否将图像转换为灰度图像。接着，将likelihood_map中的NaN值设为0，最后通过getCenterLikelihood函数计算得到response_pwp，该值表示图像块中每个像素点作为目标中心的可能性。