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');、
时间: 2024-03-04 17:51:48 浏览: 10
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。
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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
这段代码实现了一个目标跟踪算法中的直方图模型更新过程。其中包括以下几个步骤:
1. 根据当前目标的位置和大小(target_sz),计算出目标所在区域的背景区域(bg_area)和前景区域(fg_area)。
2. 根据背景区域和前景区域,构建出对应的背景掩膜(bg_mask)和前景掩膜(fg_mask)。其中,背景掩膜为全局为真值,除了目标所在区域为假值,前景掩膜则相反。
3. 将背景掩膜和前景掩膜进行缩放,使其与目标尺寸一致(norm_area)。
4. 根据背景掩膜和前景掩膜,计算出对应的背景直方图(bg_hist)和前景直方图(fg_hist)。
5. 根据输入参数new_model,判断是需要从零开始训练模型,还是需要更新已有模型。
6. 如果需要从零开始训练模型,则直接计算出新的背景直方图(bg_hist_new)和前景直方图(fg_hist_new)。
7. 如果需要更新已有模型,则根据输入参数learning_rate_pwp,计算出加权平均后的新的背景直方图(bg_hist_new)和前景直方图(fg_hist_new)。其中,learning_rate_pwp为学习率,用于平衡新旧模型的重要性。
总的来说,这段代码实现了一个简单的基于直方图的目标跟踪算法中的模型更新过程,通过不断地调整背景和前景直方图,以适应目标在图像中的变化。
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。