在此基础上，使用动态图像处理技术中的二维运动估计技术，结合背景更新模型提出了一种车辆跟踪算法。
On this basis, combined with one sort of background updating model, vehicle tracking algorithm is presented through using two-dimension motion estimation technique in dynamic image processing.
本文研究了背景相减算法、差分法、基于光流场的运动估计和基于主动形状模型的目标跟踪算法。
In the thesis, the algorithm of background subtracting, frame difference algorithm, motion estimation based on optical flow field and object tracking based on active shape contour are investigated.
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.
帧间全局运动估计模块的设计目标是计算相邻帧的背景的相对运动的矢量，这一矢量被称为全局运动矢量。
The object of global motion estimation module is to calculate the relative motion vector of adjacent frames' background, that is, the global motion vector.
首先讨论了针对运动量较小的、包含有大量背景块的视频序列的快速运动估计算法，包括整像素和半像素快速算法。
The fast integer pixel motion estimation algorithm for video sequences which are smooth, vary slowly and have many background blocks is discussed.
面向运动背景，全局运动估计是提取运动目标广泛采用的方法。
A new method is proposed for the estimation of global motion from compressed image sequences.
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.
算法从全局运动估计的基础出发，利用背景宏块运动相似性的特点快速建立背景宏块集合并采用常用的四参数全局运动估计模型估计运动参数。
From this point, the algorithm is implemented by searching and constructing a background image efficiently. With regard to global motion models, four-parameter estimation model is adopted.
采用简单的三帧差背景剪除策略检测运动目标，合并运动估计和背景补偿以加快系统反应速度。
A simple strategy named 3 frames difference background subtraction is adopted to detect moving object, and movement estimation and compensation are combined to improve processing speed.
针对利用机载运动平台对窄带微波信号进行侦测的背景，研究了被动虚拟阵列(PASA)对窄带微波信号的参数估计性能。
In this article, performance of estimation of signal parameters in PAssive Synthetic Array (PASA) is studied, focusing on the background of airborne platform intercepting narrowband microwave signals.
利用随机变量的各阶矩的性质，构造了一种基于高阶统计量的背景估计方法，并将其应用于静态背景下的运动目标检测。
An approach of background estimation is presented using characters of random variable moments and applied in moving object detection under static background.
利用随机变量的各阶矩的性质，构造了一种基于高阶统计量的背景估计方法，并将其应用于静态背景下的运动目标检测。
An approach of background estimation is presented using characters of random variable moments and applied in moving object detection under static background.
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