贝叶斯决策分析在医学步态分析中运动目标检测的应用研究

Research on Application of Bayes Decision Ruler in Moving Object Detection of Medical Gait Analysis

针对医学步态分析中的运动目标检测问题,提出基于最小错误率的贝叶斯决策规则的方法。该方法由变化检测、变化分类、前景目标提取和背景更新四部分组成。变化检测采用自适应阈值法来二值化变化点和非变化点,变化分类基于颜色共生特征向量,采用贝叶斯规则进行决策,前景对象的提取融合了时间差分法和减背景法。针对复杂场景中背景的"渐变"和"突变"情况,提出不同的背景更新策略。实验表明,该方法和包含有摇动的树枝或者灯的开关等复杂背景中能准确地提取运动目标,因此可用在医学步态分析的研究中。

This paper proposes a novel method for moving object detection from a video in medical gait analysis. It consist of four parts： change detection, change classification, foreground object abstraction, and background learning and maintenance. We use the Bayes decision rule for classification of background and foreground changes based on a special feature vector color co-occurrence feature. Foreground object abstraction fuse the classification results from both stationary and moving pixels. Learning strategies for the gradual and ＂once-off＂ background changes are proposed to adapt to various changes in background through the video. Extensive experiments on detecting foreground objects from a video containing wavering tree branches, or light open/close demonstrate that the proposed method is effective and can be used in medical gait analysis