【作者】 胡旭；

【导师】 毛晓波；

【作者基本信息】 郑州大学 ， 检测技术与自动化装置， 2013， 硕士

【摘要】 运动目标检测与跟踪是图像处理技术及计算机视觉研究的核心内容之一,广泛应用于视频监控、导航制导、医疗诊断等领域。对目标检测与跟踪方面的研究有着重要的理论意义和实用价值。基于图像灰度值的目标检测算法设计简单,应用广泛,但灰度特征下图像的色彩成分被掩盖,因此色彩空间下目标检测日益成为研究热点。本文提出了一种基于RGB分量自适应调节的目标检测方法,利用像素点红绿蓝三分量值作为特征信息组,根据背景图像的色彩组成,自适应调节三组分量所占的比重；背景差分时,分别计算三组差值,将三组差分值的绝对值之和作为像素点的差值；利用自适应阈值对差值图像进行考察,进而实现对目标的二值化分离；为了增强复杂背景下的检测效果,对均值背景法进行改进,利用去除目标区域的均值背景法建立背景模型。Mean Shift跟踪算法原理简单,实时性好,但当目标出现被遮挡的情况时容易失去跟踪目标。为此,引入卡尔曼滤波器,通过考察上一时刻对当前时刻的估计值与当前时刻的观察值,对状态变量进行更新、调整,并进一步求出此刻对下一刻的估计值。然后采用Mean Shift对预测的区域进行迭代计算。则可以对完全遮挡下的目标进行连续稳定的跟踪,显著提高了跟踪效果。更多还原

【Abstract】 As one of the core researches in the image processing and computer vision technology researches, moving target detection and tracking has been widely applied in many fields, such as video surveillance, navigation guidance, medical diagnostics, and so on.The target detection algorithm based on gray value can be designed easily and used widely, but the color components of the image are covered under the gray feature. Therefore, the target detection under the color space is becoming a research hot spot.This thesis presents an adaptive target detection method that is based on the RGB component. This method uses the RGB pixel value as the third feature information group, and it adjusts the proportion of the three components adaptively according to the background color of the image composition. When the backgrounds are different, this method calculates the difference between the three groups, and regards the sum of the absolute difference value of the three groups as the difference between pixels.This method also uses the adaptive threshold for difference image to inspect in order to achieve the target binary separation. To enhance the detection effect under the complex background, this method improves the mean background method, and it also builds the background model by the use of average of the backboard removal method of the target area.Mean Shift tracking algorithm is simple in principle and real-time, but when the target is blocked, it is easily to lose the track of the target. Therefore, the state variable is updated and modified and the estimated value of the current time to the next moment is calculated by introducing the Kalman filter to examine the estimated value of the previous time to the current time and the observed value of the current time; then the adoption of Mean Shift to carry on iterative calculation towards the predicted area can track the target under complete block continuously and stably, which promotes significantly the tracking effects.更多还原