A Robust Background Initialization Algorithm with Superpixel Motion Detection
This work addresses background initialization for computer vision applications, offering incremental improvements in robustness and speed for handling challenges like illumination changes and foreground cluttering.
The paper tackles the problem of scene background initialization from video sequences by proposing a robust algorithm based on superpixel motion detection, achieving superior or comparable performance to state-of-the-art methods on the SBMnet dataset with faster processing speed, particularly excelling in complex and dynamic scenarios.
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.