IEEE Trans Image Process. 2013 Dec;22(12):5022-35. doi: 10.1109/TIP.2013.2281423.
This paper aims toward improving background suppression from video frames by incorporating multiresolution features in Gaussian mixture model (GMM). GMM has proven its place for background modeling due to its better applicability and robustness compared with other popular methods in literature. However, GMM fails in a number of situations such as noisy and non-stationary background, slow foregrounds, and illumination variation. Extensions to GMM have also been proposed to increase accuracy in expense of increased complexity, decrease in execution speed, and reduced applicability. In view of the above, this paper aims to provide a methodology to assimilate useful multiresolution features with GMM that considerably improves the performance. The contributions of this paper are: 1) a novel framework to incorporate wavelet subbands in GMM to improve its performance; 2) an approach to incorporate variable number of clusters in the aforesaid framework; and 3) a generic platform to use any multiresolution decomposition based GMM for background suppression. Extensive experimentations on several video sequences are performed to verify the improvement in accuracy compared with conventional GMM as well as a number of state-of-the-arts approaches. Along with qualitative and quantitative analysis, justification on the use of multiresolution is provided for clarification.
本文旨在通过在高斯混合模型（GMM）中加入多分辨率特征来提高视频帧的背景抑制能力。由于与文献中其他流行方法相比，GMM 具有更好的适用性和鲁棒性，因此已被证明适用于背景建模。然而，GMM 在一些情况下会失败，例如噪声和非平稳背景、缓慢的前景和光照变化。为了提高准确性，已经提出了对 GMM 的扩展，但这是以增加复杂性、降低执行速度和降低适用性为代价的。有鉴于此，本文旨在提供一种将有用的多分辨率特征与 GMM 结合的方法，从而显著提高性能。本文的贡献有：1）一种将子波子带与 GMM 结合以提高其性能的新框架；2）在上述框架中结合可变数量聚类的方法；以及 3）一个通用平台，用于使用任何基于多分辨率分解的 GMM 进行背景抑制。本文在多个视频序列上进行了广泛的实验，以验证与传统 GMM 以及一些最先进的方法相比，在准确性方面的改进。除了定性和定量分析外，还提供了多分辨率的使用依据，以澄清其合理性。
