Scene background initialization allows the recovery of a clear image without foreground objects from a video sequence, which is generally the first step in many computer vision and video processing applications. The process may be strongly affected by some challenges such as illumination changes, foreground cluttering, intermittent movement, etc. In this paper, a robust background initialization approach based on superpixel motion detection is proposed. Both spatial and temporal characteristics of frames are adopted to effectively eliminate foreground objects. A subsequence with stable illumination condition is first selected for background estimation. Images are segmented into superpixels to preserve spatial texture information and foreground objects are eliminated by superpixel motion filtering process. A low-complexity density-based clustering is then performed to generate reliable background candidates for final background determination. The approach has been evaluated on SBMnet dataset and it achieves a performance superior or comparable to other state-of-the-art works with faster processing speed. Moreover, in those complex and dynamic categories, the algorithm produces the best results showing the robustness against very challenging scenarios.

本文提出了一种基于超像素运动检测的鲁棒背景初始化方法，旨在通过利用空间和时间特征来消除前景对象并生成可靠的背景候选项以实现图像分割。实验表明，该方法在速度方面具有优势，并可在复杂的动态场景下生成最佳结果，从而证明了它对各种挑战的鲁棒性。

一种具有超像素运动检测的鲁棒背景初始化算法