基于背景Codebook模型的前景检测算法

Foreground Detection Algorithm Based on Background Codebook Model

很多背景场景都包括复杂的运动目标,解决这种问题的较好方法是获取每个像素或者一组像素的时间序列模型,这类模型可以很好的处理时间起伏。但是,计算复杂度高而且耗时。为了获得与自适应滤波相当接近的性能。采用Codebook来建模场景中感兴趣的状态,选择RGB颜色空间模型,学习一个覆盖组成图像像素三个通道上的Codebook,可以有效的解决像素剧烈变化的问题,从而鲁棒探测出场景的前景目标。通过实验结果表明,提出的基于Codebook背景模型的目标检测方法比传统的目标检测算法具有更高的精确度和鲁棒性。

Backgrounds of many scenes include complex moving objects,a comparatively better way to solve such kind of problem is to acquire time sequence model of one or a group of pixels,which can better handle the variation with time,but it costs computational complexity. In order to obtain effect close to adaptive filtering,codebook is adopt to model background,select RGB color space to learn codebook elements covering RGB channels,and be able to solve the problem with drastic variations of pixel values,then detect the foreground objective robustly. Via experiment shows,the proposed codebook-based background model has better accuracy and robustness than other traditional objective detection algorithms.