You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
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.

Cited Abstract(s):
[1]: A novel background generation method based on nonparametric background model is presented for background subtraction. We introduce a new model, named as effect components description (ECD), to model the variation of the background, by which we can relate the best estimate of the background to the modes (local maxima) of the underlying distribution. Based on ECD, an effective background generation method, most reliable background mode (MRBM), is developed. The basic computational module of the method is an old pattern recognition procedure, the mean shift, which can be used recursively to find the nearest stationary point of the underlying density function. The advantages of this method are threefold: first, backgrounds can be generated from image sequence with cluttered moving objects; second, backgrounds are very clear without blur effect; third, it is robust to noise and small vibration. Extensive experimental results illustrate its good performance.

[2]: A novel two-stage background generation method is proposed. In the first stage, an intensity-level based statistical approach is employed to identify a variety of background variations. No background training is needed. In the second stage, a background variation based heuristic framework is designed to generate a synchronized background video sequence on-line from the surveillance video. This framework employs both static and dynamic spatial clues in the scene. The quantitative evaluations demonstrate the method can generate more realistic background images than some well-known background modeling techniques.

[3]: Background subtraction is a method typically used to segment moving regions in image sequences taken from a static camera by comparing each new frame to a model of the scene background. We present a novel non-parametric background model and a background subtraction approach. The model can handle situations where the background of the scene is cluttered and not completely static but contains small motions such as tree branches and bushes. The model estimates the probability of observing pixel intensity values based on a sample of intensity values for each pixel. The model adapts quickly to changes in the scene which enables very sensitive detection of moving targets. We also show how the model can use color information to suppress detection of shadows. The implementation of the model runs in real-time for both gray level and color imagery. Evaluation shows that this approach achieves very sensitive detection with very low false alarm rates.
To avoid parameter tuning problem, some methods use a nonparametric scheme to initialize the background image. Liu [1] present a background initialization method based on nonparametric model. The most reliable background mode is calculated based on mean shift clustering and the value is taken as the estimated background. Elgammal [3] introduce a nonparametric background modeling by estimating the probability of pixel intensity values based on kernel estimator. The pixel is then considered whether to be a background based on estimated probability. [2] , Zhang propose a two-stage background initialization method. The first stage monitors pixel intensity to identify background variations and creates a look-up table as the intensity distribution. Then in the second stage, based on whether current pixel is in the look-up table, the final background is determined.