中文摘要：

为聪明的交通监视，基于 Marr 小浪核和一种背景减法技术，基于二进制代码，分离小浪转变的一个新奇背景模型被介绍。背景模型为每个象素把紧张值的一件样品放在图象并且使用了这件样品估计象素紧张的概率密度函数。密度功能用一种新 Marr 小浪核密度评价技术被估计。因为这条途径是相当一般的，模型能接近为没有关于内在的分发形状的任何假设的象素紧张的任何分发。背景和当前的框架在二进制分离小浪领域被转变，并且背景减法在每个亚乐队被执行。在获得前景以后，阴影被一个边察觉方法消除。试验性的结果证明建议方法与许多更低的计算复杂性生产好结果并且有效地比 90% 高与精确性比率提取动人的对象，显示建议方法是为聪明的交通系统的一个有效算法。

英文摘要：

For intelligent transportation surveillance, a novel background model based on Mart wavelet kernel and a background subtraction technique based on binary discrete wavelet transforms were introduced. The background model kept a sample of intensity values for each pixel in the image and used this sample to estimate the probability density function of the pixel intensity. The density function was estimated using a new Marr wavelet kernel density estimation technique. Since this approach was quite general, the model could approximate any distribution for the pixel intensity without any assumptions about the underlying distribution shape. The background and current frame were transformed in the binary discrete wavelet domain, and background subtraction was performed in each sub-band. After obtaining the foreground, shadow was eliminated by an edge detection method. Experimental results show that the proposed method produces good results with much lower computational complexity and effectively extracts the moving objects with accuracy ratio higher than 90%, indicating that the proposed method is an effective algorithm for intelligent transportation system.