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');、

这段代码主要是用于更新目标的背景模型和前景模型的直方图。具体而言，该函数根据输入的参数，生成目标所在区域的背景掩码和前景掩码，然后将这些掩码进行缩放，以适应直方图的大小。然后，该函数使用新的图像模型（new_model）和先前的背景模型（bg_hist）和前景模型（fg_hist）来更新背景模型和前景模型的直方图。更新的方式是将新模型与原模型进行加权平均，其中权重由学习率（learning_rate_pwp）控制。最终，该函数返回更新后的背景模型和前景模型的直方图（bg_hist_new和fg_hist_new）。该函数中使用了一些辅助函数，包括mexResize，用于调整掩码的大小，以及assert，用于确保输入参数的正确性。