In this paper, we present a real-time background modeling framework for moving vehicle segmentation in traffic surveillance scenes. First, we propose a new adaptive background modeling method called Balloon Model established in the color space, which fixes the error of non-moving objects in the parameter models; second, an effective shadow detection approach is developed to detect shadows in traffic scenes, which adopts feature fusion method to achieve the segmentation of vehicles. Compared to the other methods, this technique achieves a higher accuracy and is faster.%提出一种算法框架实现对交通场景中运动车辆的分割.首先,提出一种基于颜色空间的浮动气球模型,用以解决监控场景的自适应背景建模问题,该方法解决了基于参数模型的背景建模方法无法检测驻留物体的问题,并可有效适应监控场景中的光照变化以实现自适应更新；其次,针对通过背景建模和背景差分得到的运动前景区域包含运动车辆阴影问题,提出一种新的阴影检测算法,该算法采用多特征融合的方法实现了对运动车辆的分割.实验结果分析表明,与其他方法相比,该算法框架在背景建模和阴影检测方法具有较好的效果.
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