传统算法在视频背景提取中的优势：对比泛化学习算法

Regarding the comparison of methods with different backbones, the authors have used a traditional algorithm and a generalized learning algorithm to extract the background from videos. The traditional algorithm is based on established image processing techniques, whereas the generalized learning algorithm is based on machine learning techniques. The comparison between the two methods is based on their accuracy in extracting the background from videos.

In my opinion, it's more reasonable to use a traditional algorithm to extract the background from videos because it's based on established and proven image processing techniques. These techniques have been developed and refined over many years and are widely used in various applications. The generalized learning algorithm, on the other hand, may not be as effective in extracting the background from videos because it relies on training data and may not generalize well to new situations.

Moreover, the traditional algorithm is faster and more efficient than the generalized learning algorithm because it does not require training data and can process videos in real-time. This is particularly important in applications where real-time processing is required, such as surveillance systems.

Therefore, I believe that the authors' decision to compare a traditional algorithm with a generalized learning algorithm is appropriate and the results are convincing. The traditional algorithm is a reliable and efficient method for extracting the background from videos, and its performance is comparable to that of the generalized learning algorithm.