Description: 背景差異法(Frame Diference)
此方法是以前景與背景的畫面不同，依據視訊擷取的背景資料 去取得較穩定可信任的資訊，去計算前景與固定背景的像素差異情形，以整合畫素為基礎(pixel-based)與高階區域為基礎(region-based)的資訊，管理 一些視訊並允許其背景資料 的突然改變，這個混合模式嵌入了貝氏機率理 論並允許多個物件的追蹤，有些學者提出以背景差異(background subtraction)模式為基礎去探索移動物件的陰影消除，此方法採用線上初始化(on-line initialization)演算法快速地訓練出正確的背景資訊，他們強調如何有效率並正確的建立背景資訊，以防止判斷錯誤所造成的誤差，這些研究在也同樣強調如何盡快而可靠的找出未覆蓋的背景(uncovered background)資訊。
-Background difference method (Frame Diference) This method is based on foreground and background of different scenes, based on video capture background information to obtain more stable trusted information, to calculate the prospects for fixed-pixel differences in background, in order to integrate painting Su-based (pixel-based) and high-end basic region (region-based) information Platform: |
Size: 8192 |
Author:CarinaLeng |
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Description: 基于graphcut的背景建模程序，其中具备阴影去除功能，建模时对rgb三个颜色通道进行了高斯模型训练。程序需要OpenCV 1.0 的支持。-This is a C implementation of background subtraction given a set of background frames as a training set.The background model is per-pixel RGB space Gaussian, assuming independence between RGB channels.
OpenCV 1.0 is required for I/O purpose. Platform: |
Size: 14336 |
Author:taotaoking |
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Description: A common method for real-time segmentation of
moving regions in image sequences involves “background
subtraction,” or thresholding the error between
an estimate of the image without moving objects and
the current image. The numerous approaches to this
problem differ in the type of background model used
and the procedure used to update the model. This paper
discusses modeling each pixel as a mixture of Gaussians
and using an on-line approximation to update
the model. The Gaussian distributions of the adaptive
mixture model are then evaluated to determine which
are most likelyt o result from a background process.
Each pixel is classified based on whether the Gaussian
distribution which represents it most effectivelyis considered
part of the background model. Platform: |
Size: 186368 |
Author:ajinkya |
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Description: Frame difference is arguably the simplest form of background subtraction. The current frame is simply subtracted from the previous frame, and if the difference in pixel values for a given pixel is greater than a threshold Ts, the pixel is considered part of the foreground. Platform: |
Size: 72704 |
Author:mohammed fadhle |
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Description: Change detection by background subtraction is a
common approach to detect moving foreground. The
resulting difference image is usually thresholded to obtain
objects based on pixel connectedness and resulting blob
objects are subsequently tracked Platform: |
Size: 654336 |
Author:marco |
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Description: muanual.
his is a C implementation of background subtraction given a set of background frames as a training set.The background model is per-pixel RGB space Ga Platform: |
Size: 1623040 |
Author:lê |
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Description: 针对背景差法易受外界环境因素影响的缺点, 提出了一种基于改进K-均值聚类的背景建模方法。通过比较任意样本与该像素位置处的子类中心之间的距离, 对各个像素的观察值进行聚类, 并在聚类过程中逐步确定其类别数。一段时间的学习之后, 样本数最多的子类就构成了背景模型。仿真结果表明, 该算法即使在运动目标存在的情况下也能准确的提取出实际的
背景, 而且显著地降低了系统的存储量。-Aimed at the disadvantage that background subtraction was liable to be affected by outside environment,
a background modeling method based on the improved K-mean clustering was provided. By comparing the distances
between certain sample and sub-class center of the pixel, observation values of the pixels were clustered and the
number of the clusters was determined during the clustering process.. After learning for a period, background model
was built by sub-class with the maximum samples. Simulations show that actual background can be extracted accurately
by the algorithm even when moving targets are existent and the memory cost of the system is reduced dramatically Platform: |
Size: 1495040 |
Author: |
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Description: 将背景减法中应用的基于像素的背景建模方法分为递归和非递归两类，分别对它们的算法进行综述，总结它们适用的场合和更新状况 并对各种算法性能从精确度、时、空复杂度3 个方面进行比较 最后对背景建模方法的发展和应用方向作总
结和展望。-Pixel－based background modeling methods which generaly used in the background subtraction are divided into two types: recursive and nonrecursive．
Firstly，the background modeling mothods are reviewed，and their possible applications and update status are summarized． Secondly，the performance of methods are compared arrording to their accuracy，time complexity and space complexity． Finally，the development and application of background modeling method are summaried and outlooked． Platform: |
Size: 176128 |
Author: |
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Description: My implementation of Stauffer & Grimson's GMM(Background subtraction
Drawbacks of Stauffer & Grimson's GMM
Since its pixel based model fitting,
a) lots of unwanted small blobs are scattered all over the frame. e.g. UCSD demo
b) main blobs aren't completely foreground. e.g. CVC, Barcelona Univ demo) Platform: |
Size: 26624 |
Author:hamed tirandaz
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Search - background subtraction pixel