空时自适应混合高斯模型复杂背景运动目标检测

为克服传统基于单像素建模方法存在的缺陷，解决复杂背景下的运动目标检测问题，将视频图像序列的空间信息引入背景建模过程中，研究了邻域更新、二维联合直方图信息熵判别、空时平滑等方法.采用引导滤波方法对视频图像进行预处理，以消除图像噪声，并保留图像中目标的边缘信息，给出了算法处理流程和实现步骤.在不同的评测数据库及现实捕获的视频图像上进行了定性及定量实验，结果表明，本文提出的算法在目标检测准确度和完整性等指标上优于传统的同类型算法，为复杂背景环境下的运动目标检测提供了一种新的解决方法. 

Spatio-Temporal Adaptive Mixture of Gaussians for Moving Objects Detection in Complex Background Scenes

To effectively resolve the problem of moving objects detection in complex background scenes and overcome the disadvantages of the traditional single-pixel based modeling method,spatial information of video image sequence was introduced into background modeling process.Several methods were studied,including neighborhood updating,two-dimensional joint histogram information entropy judgment,spatio-temporal smoothing etc.Firstly,video images were pre-processed with guided filter algorithm to remove noise and preserve edge information of the interested objects.And then,implementation procedure and processing steps were presented.Finally,some qualitative and quantitative experiments and comparison analysis were carried out based on different benchmark datasets and reality video frames.Results show that,the proposed method outperforms other traditional methods for moving object detection.The method provides a novel approach for the problem of moving objects detection in complex scenes.