The moving target detection method based on the three frame difference and background difference method was proposed, which is simply and fast to realize.
提出了利用三帧差分法和背景差分法对运动目标进行检测的方法，该运动检测的方法实现比较简单、速度快。
The result shows this algorithm is better than frame difference method, background difference method and road mark method.
结果显示该算法明显优于帧差法、背景差法、路面标记法。
Moreover, we present the adjust method for macroblock quantization parameters considering of the difference of foreground and background and blocking effects.
同时，考虑到前景和背景的差异以及块效应，提出了宏块级量化的调整方法。
After divided, the foreground and background regions which have biggish gray difference will belong to different regions, therefore the method can solve the problem mentioned before.
本文的方法以图像分割之后的区域为匹配基元，灰度相差较大的前景和背景区域分割之后将归属于不同区域，所以能较好的解决该问题。
The background difference is important for segmenting mobile objects. But this method highly depends on background quality and easily regards moving shadows as objects.
背景差分法是一种重要的运动目标分割方法，但是其不仅对背景质量的要求较高，且易将运动阴影误检测为前景目标。
Based on the traditional difference algorithm, a new background construction and updating method is proposed.
在传统的差分算法基础上，提出了一种新的背景的建立和更新的解决方法。
A new method is proposed for fast moving objects detecting based on bitmap difference dealing with the object with complicated background and great amount of data treatment.
针对移动目标检测过程中，背景信息复杂并且信息处理量大的问题，提出了一种对检测区的位图进行差影计算，从而快速检测出移动目标的方法。
According to the imaging difference of target, background clutter and noise, a moving small target detection method based on spacial high-pass filtering and N-frame track accumulating is presented.
根据目标、背景干扰和噪声在红外序列图像中的差异，提出了一种基于空间高通滤波和时间域上N帧轨迹积累的运动小目标检测方法。
Compared with the results based on traditional gray background difference, the method in this thesis has realized precise tracking of single object and multi-object with simple occlusion.
通过与传统的基于灰度背景差分方法的结果进行对比发现，本文采用的方法可以比较精确的实现对单个和多个目标以及简单遮挡情况下的跟踪。
To detect small moving targets in complex visible image background clutter, the spatial-temporal difference method is presented.
针对可见光图像背景下的运动小目标检测，提出了一种空时域联合差分的检测方法。
The paper introduces several methods for vision-surveillance using background subtraction, frame-difference method, symmetrical differencing and a method based on RGB image.
介绍了在视觉监控领域经常用到的几种运动人体检测算法，如背景减除法、帧差法、对称差分法、基于RGB图像的运动检测法。
This method bears not only the characteristic "exact to survey object" of background subtraction but also the characteristic "robust to lighting variations" of frame difference.
该方法把背景差法的“准确监测物体”与帧间差法的“对光线具有较强的适应性”结合起来。
The moving target detection method based on the three frame difference and background difference method was proposed, which is simply and fast to realize.
在对帧间差分算法进行分析的基础上，建立了基于帧间差分算法的目标探测识别系统，提出了针对该算法的脉冲光干扰方法。
A method is given to count the minimum temperature difference between target and background under given conditions on the perspective of countermeasure Forward Looking Infrared system (FLIR).
从对抗前视红外系统的角度，给出了满足特定要求时系统感知目标所需的最小温差的一种计算方法，并对计算方法作了具体推导。
Temporal difference as a efficacious method, is the primary content of the discourse. We will detailedly research the method of time average and pick-up background base on gauss model(s).
背景差法作为检测前景目标的一种有效方法，是本课题研究的重点内容，将分别对背景差法中的时间平均法和基于单（多）高斯模型的背景提取法进行详细研究。
According to the study of main motion detection and moving objects extraction methods, a new method combining frame-difference and background-difference was put forward.
在对视频监控中运动目标检测识别常用算法进行研究的基础上，本文提出了一种新的基于两帧差分法和背景差分法相结合的运动目标检测方法。
Firstly, picking up of the foreground is based on the method of background difference. Secondly, choosing a proper median filter is significant to suppress the noise interference.
其次采用合适空间尺寸的空间域非线性平滑滤波器进行滤波处理，对噪声干扰的抑制能力较强，能起到较好作用；
Firstly, picking up of the foreground is based on the method of background difference. Secondly, choosing a proper median filter is significant to suppress the noise interference.
其次采用合适空间尺寸的空间域非线性平滑滤波器进行滤波处理，对噪声干扰的抑制能力较强，能起到较好作用；
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