SuBSENSE

SuBSENSE is a background subtraction algorithm used for foreground detection in video sequences. It is designed to operate in challenging conditions such as dynamic backgrounds, illumination changes, and noisy environments, making it suitable for surveillance and other real-time monitoring tasks. The method is known for its robustness and adaptability across a wide range of scenes.

The core idea of SuBSENSE is to maintain a per-pixel background model composed of a set of recent pixel values and to determine foreground versus background by comparing the current frame to these samples with locally adaptive thresholds. The thresholds for distinguishing background from foreground are not fixed; they are adjusted based on the recent history and local activity, allowing the model to respond to gradual lighting shifts and to changes in the scene texture. In addition to pixel-wise comparisons, SuBSENSE incorporates information from the spatial neighborhood of each pixel. This neighborhood-aware decision helps reduce isolated misclassifications and improves stability in dynamic areas of the frame.

The algorithm includes an online learning process that updates the background model over time. Updates are performed with a balance between responsiveness to real changes and resistance to short-term noise, helping to prevent rapid fluctuations between foreground and background labels. SuBSENSE is designed to be computationally efficient enough for real-time processing on typical video hardware and has been shown to perform well on challenging benchmarks compared with conventional methods.

In practice, SuBSENSE is used for tasks requiring reliable foreground segmentation in surveillance video and other applications where dynamic backgrounds and lighting variations are common.