基于动态阈值的核密度估计前景检测算法

为解决煤层气开采(CBM)现场中抽水机往复运动和风吹草动等动态环境对前景检测的干扰及核密度估计(KDE)目标检测法实时性差的问题,提出了一种改进核密度估计前景检测算法。该方法先用背景差分法(BS)融合三帧差算法将图像分割成动态背景区与非动态背景区,对于动态背景区再用核密度算法分割前景。分割前景时提出了一种新的动态阈值求取方法,综合了相邻样本绝对差均值和样本方差来确定窗宽,并用定时更新与实时更新相结合的策略更新第二背景模型,在替换样本时用随机抽取策略代替先进先出(FIFO)方式。仿真结果表明,改进核密度估计算法与核密度估计法和背景差分核密度估计(BS-KDE)法相比,平均每帧图像算法耗时分别降低了94.18%和15.38%,识别的运动目标也更为完整。实验结果表明所提算法在煤层气开采场景中能准确检测到前景,并基本满足标清视频监控实时性要求。     

Modeling and segmentation of floating foreground and background in videos

We propose an on-line algorithm to segment foreground from background in videos captured by a moving camera. In our algorithm, temporal model propagation and spatial model composition are combined to generate foreground and background models, and likelihood maps are computed based on the models. After that, an energy minimization technique is applied to the likelihood maps for segmentation. In the temporal step, block-wise models are transferred from the previous frame using motion information, and pixel-wise foreground/background likelihoods and labels in the current frame are estimated using the models. In the spatial step, another block-wise foreground/background models are constructed based on the models and labels given by the temporal step, and the corresponding per-pixel likelihoods are also generated. A graph-cut algorithm performs segmentation based on the foreground/background likelihood maps, and the segmentation result is employed to update the motion of each segment in a block; the temporal model propagation and the spatial model composition step are re-evaluated based on the updated motions, by which the iterative procedure is implemented. We tested our framework with various challenging videos involving large camera and object motions, significant background changes and clutters.     

Multilayer background modeling under occlusions

A multilayer background modeling technique is presented for video surveillance. Rather than simply classifying all features in a scene as either dynamically moving foreground or long-lasting, stationary background, a temporal model is used to place each scene object in time relative to each other. Foreground objects that become stationary are registered as layers on top of the background layer. In this process of layer formation, the algorithm deals with ”fake objects” created by moved background, and noise created by dynamic background and moving foreground objects. Objects that leave the scene are removed based on the occlusion reasoning among layers. The technique allows us to understand and visualize a scene with multiple objects entering, leaving, and occluding each other at different points in time. This scene understanding leads to a richer representation of temporal scene events than traditional foreground/background segmentation. The technique builds on a low-cost background modeling technique that makes it suitable for embedded, real-time platforms.     

基于码书的运动目标检测方法

运动目标检测是视频监控系统需要解决的关键问题之一。背景差法是固定的单摄像头监控中常用的一种运动目标检测方法,其核心是背景的构造。提出了一种基于码书的背景构造方法,它能在有限的存储空间开销下使用长时间的图像序列估计背景模型。该方法首先对每一个像素点的抽样进行聚类分析,即构造初始的码书;然后根据背景模型的条件从中挑选出合格的码字构造背景码书;最后通过判断当前的像素值是否可以划归于背景码书以区分背景点和前景点,同时做相应的更新。实验结果表明,即使背景本身存在运动和光照条件发生变化,该方法构造的背景也能有效检测运动目标。     

基于图形处理器的视频二值概率分割

针对现有视频二值分割算法分割性能过低的问题,提出了一种基于GPU的视频实时二值概率分割算法.该算法通过规范化视频帧中每个像素属于前景类和背景类的概率大小,实现了基于二次马尔可夫测量场(QMMF)模型的视频实时二值概率分割.首先分别为不同场景的视频帧提出了两种概率模型,即静态背景概率模型(SBLM)和动态背景概率模型(UBLM);然后,通过光照矫正算法颜色转换、阴影抑制算法阴影检测以及伪装检测算法来计算每个像素属于背景类的概率值;最后,通过Gauss-Seidel模型迭代计算出了使能量函数取得最小值的背景概率值进而得到像素的二值化值.此外,为了提高算法分割的准确性,该算法包含了对光照突变、投射阴影以及伪装情况的实时处理.同时,为了满足算法的实时性要求,在NVIDIA GPU上并行实现了该算法.验证了所提算法的分割性能即算法分割的正确性,测试了算法的GPU执行时间.实验结果表明,在算法分割完整性方面ViBe+和GMM+的平均漏检率和平均误检率分别是QMMF的3倍和6倍;在算法执行时间方面ViBe+和GMM+的平均GPU执行时间大约是QMMF的1.3倍.此外,还计算了QMMF算法的GPU加速比约为76.8.     

Density-based multifeature background subtraction with support vector machine

Background modeling and subtraction is a natural technique for object detection in videos captured by a static camera, and also a critical preprocessing step in various high-level computer vision applications. However, there have not been many studies concerning useful features and binary segmentation algorithms for this problem. We propose a pixelwise background modeling and subtraction technique using multiple features, where generative and discriminative techniques are combined for classification. In our algorithm, color, gradient, and Haar-like features are integrated to handle spatio-temporal variations for each pixel. A pixelwise generative background model is obtained for each feature efficiently and effectively by Kernel Density Approximation (KDA). Background subtraction is performed in a discriminative manner using a Support Vector Machine (SVM) over background likelihood vectors for a set of features. The proposed algorithm is robust to shadow, illumination changes, spatial variations of background. We compare the performance of the algorithm with other density-based methods using several different feature combinations and modeling techniques, both quantitatively and qualitatively.     

结合核密度估计和边缘信息的运动对象分割算法

针对前景与背景具有相似颜色时的运动对象分割问题,提出一种结合核密度估计和边缘信息的分割算法.在前景和背景建模阶段使用颜色信息的基础上,引入边缘信息来构造前景和背景的概率模型;然后在马尔可夫随机场框架下引入与概率模型有关的似然能量项,以及反映空域连续性和时域一致性的能量项,并利用图切割方法来获得可靠的运动对象分割结果.实验结果证明,对于前景与背景具有相似颜色的视频序列,该算法降低了对象分割误差,显著地提高了整个序列中对象分割的鲁棒性.     

自适应混合高斯背景模型的改进

对自适应混合高斯背景模型进行了改进，将背景重构和前景消融时间控制机制整合到传统自适应混合高斯背景模型中，以提高运动分割的质量。背景重构算法从含有运动物体的动态场景视频序列中重构静态背景图像，然后用重构的静态背景图像初始化自适应混合高斯背景模型；而前景消融时间控制机制则使运动物体停止时的前景消融时间独立于背景模型的学习速率，从而可以根据需要调节前景消融的持续时间。实验结果表明了算法的有效性。     

Background subtraction by combining Temporal and Spatio-Temporal histograms in the presence of camera movement

Background subtraction is the classical approach to differentiate moving objects in a scene from the static background when the camera is fixed. If the fixed camera assumption does not hold, a frame registration step is followed by the background subtraction. However, this registration step cannot perfectly compensate camera motion, thus errors like translations of pixels from their true registered position occur. In this paper, we overcome these errors with a simple, but effective background subtraction algorithm that combines Temporal and Spatio-Temporal approaches. The former models the temporal intensity distribution of each individual pixel. The latter classifies foreground and background pixels, taking into account the intensity distribution of each pixels’ neighborhood. The experimental results show that our algorithm outperforms the state-of-the-art systems in the presence of jitter, in spite of its simplicity.     

A Universal Foreground Segmentation Technique using Deep-Neural Network

Multimedia Tools and Applications - Background subtraction is generally used for foreground segmentation (moving object detection) from video sequences. Several background subtraction methods have...     

Background subtraction via incremental maximum margin criterion: a discriminative subspace approach

Background subtraction is one of the basic low-level operations in video analysis. The aim is to separate static information called “background” from the moving objects called “foreground”. The background needs to be modeled and updated over time to allow robust foreground detection. Recently, reconstructive subspace learning models, such as principal component analysis (PCA) have been used to model the background by significantly reducing the data’s dimension. This approach is based on the assumption that the main information contained in the training sequence is the background meaning that the foreground has a low contribution. However, this assumption is only verified when the moving objects are either small or far away from the camera. Furthermore, the reconstructive representations strive to be as informative as possible in terms of well approximating the original data. Their objective is mainly to encompass the variability of the training data and so they give more effort to model the background in an unsupervised manner than to precisely classify pixels as foreground or background in the foreground detection. On the other hand, discriminative methods are usually less adapted to the reconstruction of data; although they are spatially and computationally much more efficient and often give better classification results compared with the reconstructive methods. Based on this fact, we propose the use of a discriminative subspace learning model called incremental maximum margin criterion (IMMC). The objective is first to enable a robust supervised initialization of the background and secondly a robust classification of pixels as background or foreground. Furthermore, IMMC also allows us an incremental update of the eigenvectors and eigenvalues. Experimental results on different datasets demonstrate the performance of this proposed approach in the presence of illumination changes.     

利用背景聚类的快速前景分割算法

提出一种利用背景聚类的快速前景分割算法。该算法首先通过一种专门用于背景聚类的无监督模糊聚类方法将历史像素值进行聚类,继而用高斯成分来模拟每一个聚类,构建了基于聚类的时间域的背景模型。前景的分割则采用阈值化方法对像素属于背景的概率进行二分化处理。由于该方法能够根据场景自适应确定背景为单模或多模分布,避免了耗时的背景模型构建和更新过程,因而减少了内存使用量并提高了检测速度。对于多种场景下的不同视频进行实验,结果表明该算法能够在保持检测精度的同时,大幅提高检测速度。     

基于FCM和图割的交互式图像分割方法

为了提高在前景和背景颜色相似情况下图像的分割效果,提出了一种基于模糊C均值聚类（FCM）和图割的交互式图像分割方法。首先,利用分水岭算法对图像进行预处理,将图像分成多个小区域,用区域代替像素点进行分析。然后,采用模糊C均值算法对用户标记的前景区域和背景区域分别进行聚类分析,挖掘用户交互所提供的隐藏信息。用未标记区域的颜色分量到前景区域及背景区域类心的最小距离表示相似能量,用未标记区域与其相邻区域的相关性表示先验能量。最后,利用最大流/最小割算法求能量函数的全局最优解。与其他方法相比,该文方法具有较好的分割性能,能从前景背景相似的图像中较精确地提取感兴趣的物体,且用户操作简单。     

A dynamic conditional random field model for foreground and shadow segmentation

This paper proposes a dynamic conditional random field (DCRF) model for foreground object and moving shadow segmentation in indoor video scenes. Given an image sequence, temporal dependencies of consecutive segmentation fields and spatial dependencies within each segmentation field are unified by a dynamic probabilistic framework based on the conditional random field (CRF). An efficient approximate filtering algorithm is derived for the DCRF model to recursively estimate the segmentation field from the history of observed images. The foreground and shadow segmentation method integrates both intensity and gradient features. Moreover, models of background, shadow, and gradient information are updated adaptively for nonstationary background processes. Experimental results show that the proposed approach can accurately detect moving objects and their cast shadows even in monocular grayscale video sequences.     

复杂背景下运动目标的改进实时检测算法

针对复杂背景下运动目标检测难度大、算法实时性差的问题,提出了一种改进的运动目标实时检测算法.融合背景差分、帧间差分和边缘检测的信息,划定目标区域提取完整的目标轮廓.针对图像光照突变的问题,改进了运行期均值背景更新策略,在背景更新时同步更新前景分割阈值.使用复杂背景下毛细管粘度计液位检测视频验证算法证明,边缘融合方法和实时阈值更新的背景更新算法能够克服背景复杂、光照变化、运动干扰等带来的影响,提高运动目标检测精度,实现实时检测.     

Clothing segmentation using foreground and background estimation based on the constrained Delaunay triangulation

This paper proposes a new clothing segmentation method using foreground (clothing) and background (non-clothing) estimation based on the constrained Delaunay triangulation (CDT), without any pre-defined clothing model. In our method, the clothing is extracted by graph cuts, where the foreground seeds and background seeds are determined automatically. The foreground seeds are found by torso detection based on dominant colors determination, and the background seeds are estimated based on CDT. With the determined seeds, the color distributions of the foreground and background are modeled by Gaussian mixture models and filtered by a CDT-based noise suppression algorithm for more robust and accurate segmentation. Experimental results show that our clothing segmentation method is able to extract different clothing from static images with variations in backgrounds and lighting conditions.     

Layered Representation of a Video Shot with Mosaicing

: This paper presents a motion segmentation method useful for representing efficiently a video shot as a static mosaic of the background plus sequences of the objects moving in the foreground. This generates an MPEG-4 compliant, layered representation useful for video coding, editing and indexing. First, a mosaic of the static background is computed by estimating the dominant motion of the scene. This is achieved by tracking features over the video sequence and using a robust technique that discards features attached to the moving objects. The moving objects get removed in the final mosaic by computing the median of the grey levels. Then, segmentation is obtained by taking the pixelwise difference between each frame of the original sequence and the mosaic of the background. To discriminate between the moving object and noise, temporal coherence is exploited by tracking the object in the binarised difference image sequence. The automatic computation of the mosaic and the segmentation procedure are illustrated with real sequences experiments. Examples of coding and content-based manipulation are also shown. Received: 31 August 2000, Received in revised form: 18 April 2001, Accepted: 20 July 2001     

Real-time robust background subtraction under rapidly changing illumination conditions

Fast robust background subtraction under sudden lighting changes is a challenging problem in many applications. In this paper, we propose a real-time approach, which combines the Eigenbackground and Statistical Illumination method to address this issue. The first algorithm is used to reconstruct the background frame, while the latter improves the foreground segmentation. In addition, we introduce an online spatial likelihood model by detecting reliable background pixels. Extensive quantitative experiments illustrate our approach consistently achieves significantly higher precision at high recall rates, compared to several state-of-the-art illumination invariant background subtraction methods.     

Probabilistic fusion of stereo with color and contrast for bilayer segmentation

This paper describes models and algorithms for the real-time segmentation of foreground from background layers in stereo video sequences. Automatic separation of layers from color/contrast or from stereo alone is known to be error-prone. Here, color, contrast, and stereo matching information are fused to infer layers accurately and efficiently. The first algorithm, layered dynamic programming (LDP), solves stereo in an extended six-state space that represents both foreground/background layers and occluded regions. The stereo-match likelihood is then fused with a contrast-sensitive color model that is learned on-the-fly and stereo disparities are obtained by dynamic programming. The second algorithm, layered graph cut (LGC), does not directly solve stereo. Instead, the stereo match likelihood is marginalized over disparities to evaluate foreground and background hypotheses and then fused with a contrast-sensitive color model like the one used in LDP. Segmentation is solved efficiently by ternary graph cut. Both algorithms are evaluated with respect to ground truth data and found to have similar performance, substantially better than either stereo or color/contrast alone. However, their characteristics with respect to computational efficiency are rather different. The algorithms are demonstrated in the application of background substitution and shown to give good quality composite video output.     

基于视觉背景提取的自适应运动目标提取算法

在复杂场景下的视频运动目标提取是视频分析技术的首要工作。为了解决前景运动目标提取的精确度不高的问题,提出一种基于视觉背景提取(ViBE)的改进视频运动目标提取算法(ViBE⁺)。首先,在背景模型初始化阶段采用像素的菱形邻域来简化样本信息;其次,在前景运动目标提取阶段引入自适应分割阈值来适应场景的动态变化;最后,在更新阶段提出背景重建和调整更新因子方法来处理光照变化的情形。实验结果表明,对于复杂视频场景LightSwitch的运动目标提取结果在相似度指标上,改进后的算法与混合高斯模型(GMM)算法、码本模型算法以及原始ViBE算法相比,分别提高了1.3倍、1.9倍以及3.8倍。所提算法能够在有效时间内对复杂场景具有较好的自适应性,且性能明显优于对比算法。