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An improved scheme for multifeature-based foreground detection using challenging conditions
Digital Signal Processing ( IF 2.9 ) Pub Date : 2021-03-24 , DOI: 10.1016/j.dsp.2021.103030
Subrata Kumar Mohanty , Suvendu Rup , M.N.S. Swamy

Detecting an accurate foreground from a video frame is a critical task under different complex situations such as sudden changes in illuminations, relocation of background objects, shadow, low contrast videos and dynamic backgrounds (like waving tree, rippling of water etc.). Most of the existing schemes utilize a single feature-based foreground detection approach, which in turn is hard to apply under aforementioned complex situations. In order to mitigate this issue and properly exploit the different characteristics of the pixels, the present work proposes an efficient foreground detection scheme for better segmenting the foreground. In the proposed scheme, first the texture features are first extracted utilizing cross-diagonal texture matrix (CDTM), which essentially combines the merits of both the gray level co-occurrence matrix (GLCM) and the texture spectrum (TS) to provide a complete texture information about a frame. The color and gray value features of the pixel along with texture features are utilized for the feature vector generation. Second, during background modeling phase, the similarity distance measure is computed employing the Canberra distance between the mean feature vector of the current frame and the model. Finally, a method for adaptively selecting the threshold value is proposed, instead of choosing heuristically to correctly classify the foreground and background pixels under the dynamic background condition when background pixels changing frequently.

Experiments are conducted using a wide variety of indoor and outdoor video sequences under various different challenging conditions and the results compared with that of the existing state-of-the-art methods. From the experimental results, it is shown that the proposed scheme outperforms the existing schemes in terms of the quantitative as well as qualitative measures.



中文翻译:

使用挑战性条件的基于多特征的前景检测的改进方案

在不同的复杂情况下,例如照明的突然变化,背景对象的重新定位,阴影,低对比度的视频和动态背景(如挥舞的树,水的涟漪等),从视频帧中检测出准确的前景是一项至关重要的任务。大多数现有方案利用基于单个特征的前景检测方法,这又很难在上述复杂情况下应用。为了减轻这个问题并适当地利用像素的不同特性,本工作提出了一种有效的前景检测方案,以更好地分割前景。在提出的方案中,首先使用交叉对角纹理矩阵(CDTM)提取纹理特征,它实质上结合了灰度共生矩阵(GLCM)和纹理光谱(TS)的优点,以提供有关帧的完整纹理信息。像素的颜色和灰度值特征以及纹理特征被用于特征向量生成。其次,在背景建模阶段,使用当前帧的平均特征向量与模型之间的堪培拉距离来计算相似距离度量。最后,提出了一种在背景像素频繁变化的情况下自适应选择阈值的方法,而不是在动态背景条件下进行启发式选择以正确分类前景和背景像素。像素的颜色和灰度值特征以及纹理特征被用于特征向量生成。其次,在背景建模阶段,使用当前帧的平均特征向量与模型之间的堪培拉距离来计算相似距离度量。最后,提出了一种自适应选择阈值的方法,而不是在背景像素频繁变化的情况下,在动态背景条件下进行启发式选择,对前景像素和背景像素进行正确分类。像素的颜色和灰度值特征以及纹理特征被用于特征向量生成。其次,在背景建模阶段,使用当前帧的平均特征向量与模型之间的堪培拉距离来计算相似距离度量。最后,提出了一种自适应选择阈值的方法,而不是在背景像素频繁变化的情况下,在动态背景条件下进行启发式选择,对前景像素和背景像素进行正确分类。

在各种不同的挑战性条件下,使用各种室内和室外视频序列进行了实验,并将结果与​​现有的最新方法进行了比较。从实验结果可以看出,该方案在定量和定性方面均优于现有方案。

更新日期:2021-03-27
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