一种区域级运动目标检测方法  

A Region-Level Moving Object Detection Method

作　　者：王欢[1] 任明武[1] 杨静宇[1] 

机构地区：[1]南京理工大学计算机科学与技术学院,南京210094

出　　处：《模式识别与人工智能》2009年第5期689-696,共8页Pattern Recognition and Artificial Intelligence

基　　金：国家自然科学基金重点项目(No.60632050);国家自然科学基金项目(No.60503026)资助

摘　　要：传统运动目标检测方法通常在像素或硬性划分的区域上实现.文中使用分水岭变换自动将图像划分成灰度一致性区域,并以一致性区域为基元进行运动目标检测.针对分水岭变换的过分割问题,在多步形态学梯度图像上进行变换.针对运动目标检测的低虚警率和高实时性要求,直接考察待检测图像中每一个一致性区域与一组背景图像中对应区域间的差异程度,设计灰度差异、颜色畸变及相邻区域间的灰度关系准则综合判断各区域是前景还是背景.该方法与流行的检测方法相比具有较低的虚警率,避免区域级检测方法中的硬性分块问题,同时又具有一定的处理速度.多个室内和室外标准图像序列的测试证明该算法的有效性.Conventional moving object detection methods are usuaUy based on pixel or hard-divided-blocks. In this paper, watershed transformation is applied to segment an image into homogenous regions adaptively, and these regions are then used as minimal processing cell for moving object detection. To alleviate the over-segmentation problem, watershed transformation is performed on multi-stage morphological gradient image. To meet the requirement of low false alarm and high real-time performance, a homogenous region belonging to foreground or background is judged directly by measuring intensity difference, chromaticity distortion, and intensity relation among adjacent regions between the region and its corresponding regions in a series of maintained history background image. The false alarm of the proposed approach is lower than that of the popular detection methods, and it avoids the hard block-split problem in region-based detection approaches. The processing speed of the proposed approach is also satisfactory. Experiments on several benchmark video sequences are made including both indoor and outdoor scenes, and the results demonstrate the effectiveness of the proposed algorithm.

关 键 词：分水岭变换 运动目标检测 高斯混合模型(GMM) 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]