School of Intelligent Manufacturing, Weifang University of Science and Technology, Shandong, Weifang 261000, China.
Department of Information and Communication Engineering, Hoseo University, Chungcheongnam-do, Asan 31499, Republic of Korea.
Comput Intell Neurosci. 2022 Aug 31;2022:4331351. doi: 10.1155/2022/4331351. eCollection 2022.
Foreground detection is an essential step in computer vision and video processing. Accurate foreground object extraction is crucial for subsequent high-level tasks such as target recognition and tracking. Although many foreground detection algorithms have been proposed, foreground detection in complex scenes is still a challenging problem. This paper presents a foreground detection algorithm based on superpixel and semantic segmentation. It first uses multiscale superpixel segmentation to obtain the initial foreground mask. At the same time, a semantic segmentation network is applied to separate potential foreground objects, and then use the defined rules to combine the results of superpixel and semantic segmentation to get the final foreground object. Finally, the background model is updated with the refined foreground result. Experiments on the CDNet2014 dataset demonstrate the effectiveness of the proposed algorithm, which can accurately segment foreground objects in complex scenes.
前景检测是计算机视觉和视频处理中的一个基本步骤。准确的前景目标提取对于后续的高级任务（如目标识别和跟踪）至关重要。虽然已经提出了许多前景检测算法，但复杂场景中的前景检测仍然是一个具有挑战性的问题。本文提出了一种基于超像素和语义分割的前景检测算法。它首先使用多尺度超像素分割来获得初始的前景掩模。同时，应用语义分割网络来分离潜在的前景对象，然后使用定义的规则来组合超像素和语义分割的结果，以获得最终的前景对象。最后，用细化的前景结果更新背景模型。在 CDNet2014 数据集上的实验表明，该算法能够准确地分割复杂场景中的前景对象。
