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 confidence of the effective sample is reduced, and the confidence of the invalid sample is reduced to prevent the valid sample from being updated as much as possible. The static foreground is determined by constructing the visual attention mechanism in the subsequence, and the background model is finally dynamically updated with the strategy of updating the minimum confidence samples. The core of this step is to define a visual attention mechanism to judge whether it is a background based on color saliency and similarity between background and texture. The pixel classification method based on the weighted fusion of confidence and the static foreground detection based on visual attention are used to extract moving foreground and still foreground,respectively, which are combinations of a whole. The foreground mask obtained by the pixel classification method based on the weighted fusion of confidence is used as the candidate region of static foreground detection. When the texture difference is calculated, the sample information of the background model constructed is also needed, and the background model here is the updated background model. The pixels detected as still foreground also cover the pixels that are falsely detected as background by the pixel classification method based on the confidence weighted fusion to guide the updating of model samples. In still foreground detection, when no candidate foreground region exists in the first frame of the subsequence, the static foreground detection of the current subsequence is not carried out, which improves the efficiency of the algorithm.Result In this paper,to evaluate the performance of the proposed algorithm, 10 groups of video sequences are randomly selected from the scene background modeling red-green-blue-depth(SBM-RGBD) and change detection workshops 2014(CDW2014) database for the experiment. Compared with the contrast algorithm, the proposed algorithm performs better in most video sequences and can detect static and camouflaged foreground targets. Overall, from the qualitative point of view, the proposed algorithm is better than the five other algorithms in static foreground and color camouflage detection. The recall and precision indexes of the proposed algorithm are improved by 2.66% and 1.48%, respectively, compared with the second best algorithm. Conclusion Quantitative and qualitative analyses of the experiment show that the proposed algorithm is superior to the other algorithms, achieves the accuracy and recall rate of foreground detection in the complex situation of color camouflage and presence of still objects, and achieves a better detection effect. Experimental results show that the proposed algorithm can effectively detect foreground targets in complex scenes caused by camouflage and static foreground. In addition, foreground target detection can be carried out in actual monitoring scenes.