| 引用本文: | 谢寒,蒋阳升,姚志洪,彭赛.多级分块的交通视频智能识别背景建模方法[J].哈尔滨工业大学学报,2017,49(9):40.DOI:10.11918/j.issn.0367-6234.201604141 |
| XIE Han,JIANG Yangsheng,YAO Zhihong,PENG Sai.Background modeling based on multi-level block for traffic video intelligent recognition[J].Journal of Harbin Institute of Technology,2017,49(9):40.DOI:10.11918/j.issn.0367-6234.201604141 |
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| 多级分块的交通视频智能识别背景建模方法 |
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谢寒1,2,蒋阳升1,2,姚志洪1,2,彭赛1
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(1.西南交通大学 交通运输与物流学院,成都 610031; 2.综合交通运输智能化国家与地方联合工程实验室,成都 610031)
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| 摘要: |
| 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.
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| 关键词: 背景建模 帧差法 局部二值模式 对称二值模式 码本算法 混合高斯背景建模法 |
| DOI：10.11918/j.issn.0367-6234.201604141 |
| 分类号:TP391.41 |
| 文献标识码:A |
| 基金项目:国家自然科学基金(9,5); 西南交通大学博士创新基金(YH1000212471404) |
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| Background modeling based on multi-level block for traffic video intelligent recognition |
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XIE Han1,2,JIANG Yangsheng1,2,YAO Zhihong1,2,PENG Sai1
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(1.School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 610031, China; 2.National United Engineering Laboratory of Integrated and Intelligent Transportation, Chengdu 610031, China)
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| Abstract: |
| 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.
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| Key words: background modeling frame differential method local binary pattern center-symmetric local binary pattern codebook multi-Gauss model |
