多级分块的交通视频智能识别背景建模方法

为改善视频监控中的背景建模方法对于前景目标物较多或者光线变化的复杂环境效果不太理想的缺陷,提出一种多级分块背景建模方法.该方法以间隔N帧帧差法为基础,采用多级分块,并结合对称二值模式(center-symmetric local binary pattern,CSLBP)和码本(codebook,CB)等算法建立背景模型.通过模型得出背景较为清晰和完整,为下一步进行前景目标的准确识别提供良好基础.采用设计实验检验该方法的有效性,将其与局部二值模式(local binary pattern,LBP)、CSLBP、CB以及经典的混合高斯背景建模(mixture of Gaussian,MOG)等算法进行对比分析,得出采用此方法提取的前景目标物更加完整,边界更加清晰,且无明显分块图形出现.采用评分的方法对几种方法进行综合评分,该方法评分较高.在对前景目标物的提取方法中,该方法效果较好.

Background modeling based on multi-level block for traffic video intelligent recognition

In order to improve the effect of a background model in traffic video surveillance which was not good for the complex environment with too many foreground objects or light varying, a background modeling of multi-level block was proposed. The model was based on the frame differential method with the N frames interval and multilevel block, combining with center-symmetric local binary pattern and codebook algorithm. Using the model, the background obtained is clear and unbroken, and is the base for the foreground object extraction. To test the validity of the method, the designed experiment was compared with local binary pattern, center-symmetric local binary pattern, codebook algorithm and mixture of Gaussian. The proposed model got the more complete foreground objects, more clearly boundary of the objects, and no significant block figure. We scored the methods and the proposed method got higher scores. In the foreground object extraction methods, the method we proposed had the better results.