共查询到20条相似文献,搜索用时 78 毫秒
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Real-time moving object detection is challenging for moving cameras due to the moving background. Many studies use homography matrix to compensate for global motion by warping the background model to the current frame. Then, the pixel difference between the current frame and the background model is used for background subtraction. Moving pixels are extracted by applying adaptive threshold and some post-processing techniques. On the other hand, deep learning-based dense optical flow can be efficient enough to extract the moving pixels, but it increases computational cost. This study proposes a method to enhance a classical background modeling method with deep learning-based dense optical flow. The main contribution of this paper is to propose a fusing algorithm for dense optical flow and background modeling approach. The background modeling methods are error-prone, especially with continuous camera movement, while the optical flow method alone may not always be efficient. Our hybrid method fuses both techniques to improve the detection accuracy. We propose a software architecture to run background modeling and dense optical flow methods in parallel processes. The proposed implementation approach significantly increases the method’s working speed, while the proposed fusion and combining strategy improve detection results. The experimental results show that the proposed method can run at high speed and has satisfying performance against the methods in the literature. 相似文献
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Replacing a video chat background with a landscape image can generate the realism of a user actually being in the landscape. To enhance this realism, we proposed in our previous study a background replacement method that uses a chromatic adaptation transform. This method can enhance the realism of video chat by fitting the color of the foreground image to an illuminant color of a landscape, which is used as the new background image. However, if an incorrect color of the landscape illuminant is obtained through this method, which estimates the illuminant color on the basis of a gray world assumption, the method might not enhance the realism. This is because it converts the foreground color to an incorrect color. In this paper, we therefore propose a method to estimate illuminant color on the basis of the dichromatic reflection model, which improves background replacement using the chromatic adaptation transform. We perform a subjective evaluation using 13 subjects to examine the effects of the proposed method. The results indicate that the proposed method can effectively enhance the realism of the background replacement video. 相似文献
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针对经典W4背景建模算法只能克服光照强度的微小变化以及背景的轻微运动等问题,提出了一种新的运动目标检测算法。首先,利用均值法进行背景初始化选出静止像素集合,消除背景中运动目标的干扰;其次,给定背景初始帧,用经典W4算法计算出每个像素点的最小灰度值、最大灰度值以及最大帧间差分值;然后,对每个像素点提取的最小灰度值和最大灰度值进行线性加权,并且与均值法得到的初始背景相结合建立稳定的背景模型,克服了移动、阴影、光照突变等影响;最后,比较当前帧与背景模型从而检测出准确的运动目标。实验证明,与其它均值法、经典W4算法以及混合高斯背景建模方法相比较,改进方法不仅耗时短而且取得了较为理想的检测效果。 相似文献
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Liyuan Li Weimin Huang Irene Yu-Hua Gu Qi Tian 《IEEE transactions on image processing》2004,13(11):1459-1472
This paper addresses the problem of background modeling for foreground object detection in complex environments. A Bayesian framework that incorporates spectral, spatial, and temporal features to characterize the background appearance is proposed. Under this framework, the background is represented by the most significant and frequent features, i.e., the principal features, at each pixel. A Bayes decision rule is derived for background and foreground classification based on the statistics of principal features. Principal feature representation for both the static and dynamic background pixels is investigated. A novel learning method is proposed to adapt to both gradual and sudden "once-off" background changes. The convergence of the learning process is analyzed and a formula to select a proper learning rate is derived. Under the proposed framework, a novel algorithm for detecting foreground objects from complex environments is then established. It consists of change detection, change classification, foreground segmentation, and background maintenance. Experiments were conducted on image sequences containing targets of interest in a variety of environments, e.g., offices, public buildings, subway stations, campuses, parking lots, airports, and sidewalks. Good results of foreground detection were obtained. Quantitative evaluation and comparison with the existing method show that the proposed method provides much improved results. 相似文献
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针对非高斯相关杂波背景下,移动目标检测(MTD)技术的检测性能严重下降的问题,该文基于Alpha稳定分布杂波模型和本征滤波理论,提出一种非高斯相关杂波背景下的雷达目标检测方法。该方法基于Alpha稳定分布杂波模型,通过幂变换抑制杂波的非高斯特性,以及通过分数低阶相关矩阵白化杂波,在此基础上应用本征滤波实现对目标信号的有效积累,可提高信杂比。仿真实验和实测数据验证表明,该方法在非高斯相关杂波背景下的检测性能明显优于MTD方法的性能。 相似文献
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提出了一种将剪切波变换与贝叶斯统计机理相结合的背景抑制新方法来解决红外搜索跟踪系统探测复杂空中和地面背景杂波中的弱小目标这一难题.根据红外图像中目标和背景杂波的不同分布特性,首先,采用剪切波变换对原始红外图像进行多尺度和多方向分解,获得原始图像的多尺度和方向细节特征,然后,通过应用高斯尺度混合模型进行处理,从而将红外图... 相似文献
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Moving object detection is one of the essential tasks for surveillance video analysis. The dynamic background often composed by waving trees, rippling water or fountains, etc. in nature scene greatly interferes with the detection of moving objects in the form of noise. In this paper, a method simulating heat conduction is proposed to extract moving objects from dynamic background video sequences. Based on the visual background extractor (ViBe) with an adaptable distance threshold, we design a temperature field relying on the generated mask image to distinguish between the moving objects and the noise caused by dynamic background. In temperature field, a brighter pixel is associated with more energy. It will transfer a certain amount of energy to its neighboring darker pixels. Through multiple steps of energy transfer the noise regions loss more energy so that they become darker than the detected moving objects. After heat conduction, K-Means algorithm with the customized initial clustering centers is utilized to separate the moving objects from background. We test our method on many videos with dynamic background from public datasets. The results show that the proposed method is feasible and effective for moving object detection from dynamic background sequences. 相似文献
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复杂背景图像受背景干扰后不易被识别。针对这一问题,文中提出了基于前景分割机制的卷积神经网络图像分类方法。采用全卷积神经网络对图像前景区域进行自动分割,通过图像中前景区域周围的最小边界框对其进行定位。对于定位的前景区域,构建卷积神经网络对其进行处理以区分不同的类别,从而实现复杂背景图像的分类。将提出方法在公开数据集中提取的单一背景和复杂背景图像数据集上进行对比实验,并使用迁移学习与数据增强等方法优化模型。实验结果表明,所提方法使用前景区域分割相比于仅分类CNN具有更高的准确度,且复杂背景图像上的准确度提升幅度要远大于单一背景图像。该结果说明引入前景区域分割对于复杂背景图像分类模型准确度的提升具有一定帮助,能够显著前景区域特征并减少背景因素的干扰。 相似文献
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针对云背景下红外小目标检测对背景起伏依赖性强的问题,提出采用分形方法实现不同起伏背景的小目标检测。首先利用分数布朗随机场模型对实际云背景图像建模得到分形曲线,然后通过分析小目标存在对云背景分形特性的影响,总结出云背景区域含小目标时其分形曲线面积增大的规律,据此提出了基于分形曲线面积差量的小目标检测方法。实验结果表明:在虚警率为10-3时,分形方法对弱起伏和中等起伏云背景下红外小目标检测概率达到1.0,对强起伏云背景下红外小目标检测概率达到0.6,较现有其他一些方法具有更好的检测效果和背景适应能力。 相似文献
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Yinggan Tang Weiwei Mu Xiumei Zhang Yixian Yang 《Circuits, Systems, and Signal Processing》2013,32(2):711-726
Otsu’s thresholding method is a popular and efficient method for image segmentation. However, its performance is greatly affected by noise and the population size of object and background. In this paper, a novel thresholding method is proposed based on modified fuzzy linear discriminant analysis (MFLDA). MFLDA is an extension of linear discriminant analysis to fuzzy domain, where the between-class variance is modified as the distance between the centers of background and object. The optimal threshold is selected such that the MFLDA criterion is maximized. Some images are used to test the performance of the proposed thresholding method and results reveal that the proposed method is less affected by noise, the population size of objects and background, and better segmentation results are obtained than Otsu’s method and other classical thresholding methods. 相似文献
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复杂背景下的红外弱小目标检测是目标探测领域的一个难题。为了有效抑制复杂背景的干扰,降低复杂背景所带来的虚警,提出了一种基于风险估计的恒虚警检测方法。首先,分析了背景分布的统计方法,对传统的正态分布统计方法予以改进,对滤波后的图像做局部灰度的分布统计,从而更准确地描述背景图像的分布规律。然后,在传统的恒虚警算法中加入风险估计,将背景复杂度作为风险估计判断依据,利用风险估计自适应调整分割阈值,从而达到抑制背景干扰、减少虚警和误判的目的。最后,实验结果表明:该算法可以显著减少复杂背景造成的虚警,并保证能够有效探测出弱小目标。 相似文献
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在复杂背景的红外图像中弱小目标通常淹没在高亮边缘与强杂波处,提出一种基于改进加权局部对比度的红外小目标检测方法。利用小目标的局部特性建立一种加权函数将目标与其背景邻域的差异点乘凸显目标,进而与相接背景邻域作比值运算达到抑制复杂背景的效果;通过目标的各向同性和背景的各向异性,采用六方向梯度决策法创建背景抑制模型进一步抑制高亮边缘,实现降低虚警率,提高检测率的目的;最后,通过卷积计算将两者结合,采用自适应阈值分割检测真实目标。实验结果表明,该算法在复杂背景及强杂波干扰下有较强的鲁棒性。 相似文献
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Extracting accurate foreground objects from a scene is an essential step for many video applications. Traditional background subtraction algorithms can generate coarse estimates, but generating high quality masks requires professional softwares with significant human interventions, e.g., providing trimaps or labeling key frames. We propose an automatic foreground extraction method in applications where a static but imperfect background is available. Examples include filming and surveillance where the background can be captured before the objects enter the scene or after they leave the scene. Our proposed method is very robust and produces significantly better estimates than state-of-the-art background subtraction, video segmentation and alpha matting methods. The key innovation of our method is a novel information fusion technique. The fusion framework allows us to integrate the individual strengths of alpha matting, background subtraction and image denoising to produce an overall better estimate. Such integration is particularly important when handling complex scenes with imperfect background. We show how the framework is developed, and how the individual components are built. Extensive experiments and ablation studies are conducted to evaluate the proposed method. 相似文献