然后，提出了移动目标跟踪的多背景模型。
And propose a multiple background models for detecting the moving objects.
提出一种基于背景模型的自动视频分割方法。
An automatic video segmentation algorithm based on background model is proposed in this paper.
提出了一种新的用于行人检测的背景模型建模方法。
This paper presents a new method of the background modeling for detecting passenger.
本发明可对背景模型进行细致刻画，从而提高说话人识别的准确率。
By adopting the invention, delicate depiction can be carried out on the background model, thereby improving the accuracy rate on speaker recognition.
提出了一种多模态非参数背景模型，用于背景减方法检测运动目标。
A multimodal nonparametric background model is proposed to detect moving objects by background subtraction.
通过对背景模型进行选择性的实时更新，使背景差方法更具有鲁棒性。
In order to get more robust, background model is real-time selective updated.
混合高斯模型在训练背景模型的过程中效果良好，但其收敛速度较慢。
The effect of Gaussian Mixture model used in training background model is good, but its convergence velocity is low.
一种好的背景建模方法得到的背景模型应能准确地反映真实背景的变化。
A good background model can reflect the true background and can change from time to time according to the real scene.
采用自适应高斯混合方法为背景建模的难点是对背景模型的维持与更新。
Taking the method of adaptive Gaussian mixture method can make model for background meanwhile it is a difficult point to maintain and update background model.
采用贝叶斯最大后验概率估计的方式，从统一背景模型中生成说话人模型。
We use Bayesian maximum a posteriori estimation training a speaker model from background model, to solve the problem of model miss matching in speaker verification system.
提出了一种动态更新背景模型的方法，并针对高架摄像机的抖动问题做了优化。
We propose a dynamical method of renovating the model of background, and provide a better solution to the problem of video shaking unsteadily captured by camera.
文中针对混合高斯模型不能应对光线突变的问题，提出了一种改进的背景模型。
This paper proposes an improved background subtraction method based on Gaussian mixture background model which can not deal with the problem of scene light rapid change.
目前，背景模型的更新算法很多，但算法中各参数的取值通常是依据经验而定。
At present, there are all kinds of updating algorithm, but the choose value of every parameter in algorithm is usually on the basis of experience.
摘要：背景减除法是运动目标检测的常用方法，其性能取决于所使用的背景模型。
Absrtact: background subtraction is one of the common methods in motion detection, its performance depends on the background model.
通过包含区域信息的背景模型检测目标，减少在同一背景区域中目标的误判和漏判。
With the spatial areas information, the algorithm decreases the number of small fake objects and reduces the fragmentation of objects that caused by all kinds of noise.
对基于视频的车辆检测中的背景差分法进行了研究，提出了一种新的背景模型构建算法。
Based on the research on the background subtraction used to detect vehicles on video sequence, a new algorithm for background model building is presented.
在充分研究现有运动目标检测算法的基础上，提出了一种新的非参数核密度估计背景模型。
A new background model of non-parameter kernel density estimate was presented on the basis of abundant study on algorithms of moving object detection.
针对海事场景背景复杂、干扰大等困难，提出了改进的混合高斯背景模型及运动检测方法。
Because the marine scene is complicated and interferential, a modified mixture Gaussians approach and a moving detection method are suggested.
但由于构建背景模型需要考虑光照变化等很多因素，因此开发一个好的减背景算法面临很多挑战。
However, there are many challenges in developing a good background subtraction algorithm for many factors such as changes in illumination should be considered in constructing a background model.
使用了单模态背景模型，用连通检测的方法分割出目标，取得目标信息，采用特征参数匹配的方法跟踪目标。
The information of targets is obtained using a single-mode model and connected component analysis. Target matching method is adopted in target tracking.
首先建立环境的高斯背景模型，从步态视频序列中提取轮廓图像，计算质心以及轮廓线上的点到质心的欧氏距离。
It creates Gaussian Mixture Model for each scenario, and contour of gait is extracted from binary silhouette for Euclidean distance between the centroid and any pixel on it.
实验结果表明，该方法与传统高斯混合背景模型相比，有较好的学习能力与稳定性，能提高运动目标检测的正确率。
Experimental results show that compared with moving object detection approach based on conventional Gaussian mixture model, it has a desirable stability and learning ability.
针对静态背景下的背景差法，通过研究如何得到、以及如何及时更新背景模型，增强运动目标检测随环境变化的鲁棒性，提出了多级分块的背景估计方法。
Background estimation based on block is proposed to solve the problem of building background model and updating background which can enhance the robust of moving object detection.
本教程是为那些具有编程和脚本背景，并且了解基本的计算机科学模型和数据结构的开发人员编写的。
This tutorial is for developers who have a background in programming or scripting and who understand basic computer-science models and data structures.
甚至业内人士也承认他们建立的模型无法解释为什么背景极为不同的人有时会双双坠入爱河。
Even industry insiders acknowledge their models cannot deal with the notion that people from very different backgrounds sometimes fall for one another.
当然，模型还包含了许多没有同步化的代码，这些代码提供了一个广阔的背景；例如用例，对需求的追踪性，以及其他关键的产品工件。
Of course, the model also contains many things not synthesized in code that provide a wider context; such as use cases, traceability to requirements, and other key product artifacts.
当然，模型还包含了许多没有同步化的代码，这些代码提供了一个广阔的背景；例如用例，对需求的追踪性，以及其他关键的产品工件。
Of course, the model also contains many things not synthesized in code that provide a wider context; such as use cases, traceability to requirements, and other key product artifacts.
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