结合置信度加权融合与视觉注意机制的前景检测  

Foreground detection via fusing confidence by weight and visual attention mechanism

作　　者：成科扬[1] 孙爽 王文杉 师文喜 李鹏 詹永照[1] Cheng Keyang;Sun Shuang;Wang Wenshan;Shi Wenxi;Li Peng;Zhan Yongzhao(School of Computer Science and Telecommunications Engineering,Jiangsu University,Zhenjiang 212013,China;National Engineering Laboratory for Public Safety Risk Perception and Control by Big Data,Beijing 100846,China;Xinjiang Lianhaichuangzhi Information Technology CO.,LTD.,Urumqi 830011,China)

机构地区：[1]江苏大学计算机科学与通信工程学院,镇江212013 [2]社会安全风险感知与防控大数据应用国家工程实验室,北京100846 [3]新疆联海创智信息科技有限公司,乌鲁木齐830011

出　　处：《中国图象图形学报》2021年第10期2462-2472,共11页Journal of Image and Graphics

基　　金：国家自然科学基金项目(61972183,61672268);社会安全风险感知与防控大数据应用国家工程实验室主任基金项目。

摘　　要：目的在视频前景检测中,像素级的背景减除法检测结果轮廓清晰,灵活性高。然而,基于样本一致性的像素级分类方法不能有效利用像素信息,遇到颜色伪装和出现静止前景等复杂情形时无法有效检测前景。为解决这一问题,提出一种基于置信度加权融合和视觉注意的前景检测方法。方法通过加权融合样本的颜色置信度和纹理置信度之和判断前景,进行自适应更新样本的置信度和权值;通过划分子序列结合颜色显著性和纹理差异度构建视觉注意机制判定静止前景目标,使用更新置信度最小样本的策略保持背景模型的动态更新。结果本文方法在CDW2014(change detection workshops 2014)和SBM-RGBD(scene background modeling red-green-blue-depth)数据集上进行检测,相较于5种主流算法,本文算法的查全率和精度相较于次好算法分别提高2.66%和1.48%,综合性能最优。结论本文算法提高了在颜色伪装和存在静止前景等复杂情形下前景检测的精度和召回率,在公开数据集上得到更好的检测效果。可将其应用于存在颜色伪装和静止前景等复杂情形的视频监控中。Objective In the field of intelligent video surveillance,video target detection serves as a bottom-level task for high-level video analysis technologies such as target tracking and re-recognition,and the false detection and missing detection of low-level target detection are amplified layer by layer.Therefore,improving the accuracy of foreground target detection has important research value.In the foreground detection of video,the result of pixel-level background subtraction is clear and flexible.However,the pixel-level classification method based on sample consistency cannot make full use of the pixel information effectively and obtain full foreground mask when meeting the complex situation of color camouflage and static object,such as error detection of foreground pixels and missing foreground.An algorithm is proposed based on fusing confidences with weight and visual attention to solve this problem effectively.Method The advantage of this method is to make full use of the credibility of the sample to construct the background model,combine the secondary detection of color level and texture dimension to overcome the problem of color camouflage effectively,and construct attention mechanism to detect static foreground.The proposed model contains three modules.First,considering the prospect of double-dimension missing detection,the foreground is determined by the sum of fusing with color confidence and texture confidence based on weight.The color confidence and texture confidence of strong correlation samples are summed,and then weighted sum is determined.If it is less than the minimum threshold,then it is judged as foreground;otherwise,it is background.Then,the confidence and weight of the samples are updated adaptively.For the pixels detected as background,the sample template with the minimum confidence in the model is replaced by the current pixel information.If the distance between the current frame pixel and the sample in the model is greater than the given distance threshold,then the sample is valid.The confi

关 键 词：目标检测 前景检测 置信度 颜色伪装 视觉注意 静态前景 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]