一种动态场景下基于时空信息的视频对象提取算法

在实际应用中,许多视频序列具有运动背景,使得从其中提取视频对象变得复杂,为此提出了一种基于运动估计和图形金字塔的动态场景下的视频对象提取算法。该算法首先引入了相位相关法求取运动向量,因避免了视频序列中光照变化的影响,故可提高效率和稳健性;接着再根据参数模型进行全局运动估计来得到最终运动模板;然后利用图形金字塔算法对当前模板内图像区域进行空间分割,最终提取出语义视频对象。与现有算法相比,对于从具有动态场景的视频流中提取运动对象的情况,由于使用该算法能有效地避开精准背景补偿,因而不仅节省了计算量,而且提取出来的语义对象精度较高。实验表明,无论是对动态场景中刚性还是非刚性运动物体的分割,该算法都具有较好的效果。     

Augmented reality with automatic illumination control incorporatingellipsoidal models

In applications of augmented reality like virtual studio TV production, multisite video conference applications using a virtual meeting room and synthetic/natural hybrid coding according to the new ISO/MPEG-4 standard, a synthetic scene is mixed into a natural scene to generate a synthetic/natural hybrid image sequence. For realism, the illumination in both scenes should be identical. In this paper, the illumination of the natural scene is estimated automatically and applied to the synthetic scene. The natural scenes are restricted to scenes with nonoccluding, simple, moving, mainly rigid objects. For illumination estimation, these natural objects are automatically segmented in the natural image sequence and three-dimensionally (3-D) modeled using ellipsoid-like models. The 3-D shape, 3-D motion, and the displaced frame difference between two succeeding images are evaluated to estimate three illumination parameters. The parameters describe a distant point light source and ambient light. Using the estimated illumination parameters, the synthetic scene is rendered and mixed to the natural image sequence. Experimental results with a moving virtual object mixed into real video telephone sequences show that the virtual object appears naturally having the same shading and shadows as the real objects. Further, shading and shadow allows the viewer to understand the motion trajectory of the objects much better     

一种适用于动态场景的运动目标提取新算法

在动态场景中提取运动目标是开展视频分析的关键问题,也是当前计算机视觉与图像处理技术领域中的热门课题。本文提出了一种适用于动态场景的运动目标提取新算法,算法先根据摄像机全局运动模型计算全局运动参数,再利用三帧差分法得到分割的前景。将分割为背景的像素点映射到邻近帧,求得各帧的像素点为背景时其高斯模型的均值及方差。最后利用粒子滤波预测出下一帧前景区域,计算各像素点为前景的概率,获得运动目标的视频分割结果。实验表明,本文算法有效地克服了由于全局运动模型参数估算偏差而导致的累积误差,能以更高精度实现跳水运动视频中的目标分割。     

自动分割视频序列中运动物体的新算法：多特征联合方法

视频编码标准MPEG-4增加了适于多种应用的基于视频内容的功能,为了支持这一功能和提高编码效率,MPEG-4将视频序列中的每一帧分解成视频对象面(VOP);另外,由于基于内容的视频检索和视频监控系统均期望用分割出的关键视频对象紧致地表示一个序列,同时由于视频分割技术在模式识别、计算机视觉等领域也得到了广泛的应用,因此,分割视频运动物体并跟踪运动物体的变化变得至关重要.为了对视频中运动物体进行有效的分割,在帧差图象的基础上,采用Canny边缘检测和随机信号的高阶矩检测相结合的方法,来自动分割视频序列的前景区域和背景区域,并在前景区域中应用区域生长法进行颜色分割,以精确提取运动物体的边缘;还利用边缘和颜色特征来对分割出的运动物体建立模板,用于解决非刚体运动中局部暂时停止运动的情况.实验结果表明,此方法可以有效地分割运动物体,并能跟踪运动物体的变化.     

Spatio-temporal alignment of sequences

This paper studies the problem of sequence-to-sequence alignment, namely, establishing correspondences in time and in space between two different video sequences of the same dynamic scene. The sequences are recorded by uncalibrated video cameras which are either stationary or jointly moving, with fixed (but unknown) internal parameters and relative intercamera external parameters. Temporal variations between image frames (such as moving objects or changes in scene illumination) are powerful cues for alignment, which cannot be exploited by standard image-to-image alignment techniques. We show that, by folding spatial and temporal cues into a single alignment framework, situations which are inherently ambiguous for traditional image-to-image alignment methods, are often uniquely resolved by sequence-to-sequence alignment. Furthermore, the ability to align and integrate information across multiple video sequences both in time and in space gives rise to new video applications that are not possible when only image-to-image alignment is used.     

Video background modeling: recent approaches,issues and our proposed techniques

Effective and efficient background subtraction is important to a number of computer vision tasks. We introduce several new techniques to address key challenges for background modeling using a Gaussian mixture model (GMM) for moving objects detection in a video acquired by a static camera. The novel features of our proposed model are that it automatically learns dynamics of a scene and adapts its parameters accordingly, suppresses ghosts in the foreground mask using a SURF features matching algorithm, and introduces a new spatio-temporal filter to further refine the foreground detection results. Detection of abrupt illumination changes in the scene is dealt with by a model shifting-based scheme to reuse already learned models and spatio-temporal history of foreground blobs is used to detect and handle paused objects. The proposed model is rigorously tested and compared with several previous models and has shown significant performance improvements.     

Feature-Based Sequence-to-Sequence Matching

This paper studies the problem of matching two unsynchronized video sequences of the same dynamic scene, recorded by different stationary uncalibrated video cameras. The matching is done both in time and in space, where the spatial matching can be modeled by a homography (for 2D scenarios) or by a fundamental matrix (for 3D scenarios). Our approach is based on matching space-time trajectories of moving objects, in contrast to matching interest points (e.g., corners), as done in regular feature-based image-to-image matching techniques. The sequences are matched in space and time by enforcing consistent matching of all points along corresponding space-time trajectories. By exploiting the dynamic properties of these space-time trajectories, we obtain sub-frame temporal correspondence (synchronization) between the two video sequences. Furthermore, using trajectories rather than feature-points significantly reduces the combinatorial complexity of the spatial point-matching problem when the search space is large. This benefit allows for matching information across sensors in situations which are extremely difficult when only image-to-image matching is used, including: (a) matching under large scale (zoom) differences, (b) very wide base-line matching, and (c) matching across different sensing modalities (e.g., IR and visible-light cameras). We show examples of recovering homographies and fundamental matrices under such conditions.     

Background subtraction using finite mixtures of asymmetric Gaussian distributions and shadow detection

Foreground segmentation of moving regions in image sequences is a fundamental step in many vision systems including automated video surveillance, human-machine interface, and optical motion capture. Many models have been introduced to deal with the problems of modeling the background and detecting the moving objects in the scene. One of the successful solutions to these problems is the use of the well-known adaptive Gaussian mixture model. However, this method suffers from some drawbacks. Modeling the background using the Gaussian mixture implies the assumption that the background and foreground distributions are Gaussians which is not always the case for most environments. In addition, it is unable to distinguish between moving shadows and moving objects. In this paper, we try to overcome these problem using a mixture of asymmetric Gaussians to enhance the robustness and flexibility of mixture modeling, and a shadow detection scheme to remove unwanted shadows from the scene. Furthermore, we apply this method to real image sequences of both indoor and outdoor scenes. The results of comparing our method to different state of the art background subtraction methods show the efficiency of our model for real-time segmentation.     

一种基于非线性尺度空间的视频对象分割方法

本文提出一种基于非线性尺度空间理论的视频对象分割方法。该方法首先利用非线性尺度空间理论对视频序列中的每一帧图像做多尺度分解,然后在由大尺度图像组成的序列中利用运动信息确定运动对象,最后根据每一帧图像的多尺度图像确定对象的准确边界。实验结果表明,该方法能够在复杂的自然场景中精确地分割出视频对象,具有较强的抗干扰能力。     

Two-view multibody structure-and-motion with outliers through model selection

Multibody structure-and-motion (MSaM) is the problem in establishing the multiple-view geometry of several views of a 3D scene taken at different times, where the scene consists of multiple rigid objects moving relative to each other. We examine the case of two views. The setting is the following: Given are a set of corresponding image points in two images, which originate from an unknown number of moving scene objects, each giving rise to a motion model. Furthermore, the measurement noise is unknown, and there are a number of gross errors, which are outliers to all models. The task is to find an optimal set of motion models for the measurements. It is solved through Monte-Carlo sampling, careful statistical analysis of the sampled set of motion models, and simultaneous selection of multiple motion models to best explain the measurements. The framework is not restricted to any particular model selection mechanism because it is developed from a Bayesian viewpoint: different model selection criteria are seen as different priors for the set of moving objects, which allow one to bias the selection procedure for different purposes.     

Composition and Retrieval of Visual Information for Video Databases

This paper presents a new visual aggregation model for representing visual information about moving objects in video data. Based on available automatic scene segmentation and object tracking algorithms, the proposed model provides eight operations to calculate object motions at various levels of semantic granularity. It represents trajectory, color and dimensions of a single moving object and the directional and topological relations among multiple objects over a time interval. Each representation of a motion can be normalized to improve computational cost and storage utilization. To facilitate query processing, there are two optimal approximate matching algorithms designed to match time-series visual features of moving objects. Experimental results indicate that the proposed algorithms outperform the conventional subsequence matching methods substantially in the similarity between the two trajectories. Finally, the visual aggregation model is integrated into a relational database system and a prototype content-based video retrieval system has been implemented as well.     

基于多视频的虚实融合系统

提出了一种基于多视频的虚实融合可视化系统的构建方法,旨在将真实世界中的图像和视频融合到虚拟场景中,用视频图像中的纹理和动态信息去丰富虚拟场景,提高虚拟环境的真实性,得到一种增强的虚拟环境.利用无人机采集图像来重建虚拟场景,并借助图像特征点的匹配来实现视频图像的注册.然后利用投影纹理映射技术,将图像投影到虚拟场景中.视频中的动态物体由于在虚拟环境中缺失对应的三维模型,直接投影,当视点发生变化时会产生畸变.首先检测和追踪这些物体,然后尝试使用多种显示方式来解决畸变问题.此外,系统还考虑有重叠区域的多视频之间的融合.实验结果表明,所构造的虚实融合环境是十分有益的.     

基于视差和阈值分割的立体视频对象提取

视频对象分割和提取是编码、通信以及视频检索等基于内容视频处理中的关键问题，为了从只有单一全局运动、含有重叠多对象的立体视频序列中提取对象，提出了一种基于视差分析和阈值分割的对象提取方法。该方法首先用改进的区域匹配法进行立体视差估计，并通过合理减少匹配窗的运算量及根据视差特性设定搜索路径来加快匹配速度；然后针对图像中不同的对象分别采用迭代阈值法和自适应阈值法进行二次分割；最后从阈值分割结果中提取出各个对象。实验提取出的各深度层视频对象效果良好，表明该方法是一种有效的适用于全局运动的立体视频序列对象提取方法。     

Combination of Edge Element and Optical Flow Estimates for 3D-Model-Based Vehicle Tracking in Traffic Image Sequences

A model-based vehicle tracking system for the evaluation of inner-city traffic video sequences has been systematically tested on about 15 minutes of real world video data. Methodological improvements during preparatory test phases affected—among other changes—the combination of edge element and optical flow estimates in the measurement process and a more consequent exploitation of background knowledge. The explication of this knowledge in the form of models facilitates the evaluation of video data for different scenes by exchanging the scene-dependent models. An extensive series of experiments with a large test sample demonstrates that the current version of our system appears to have reached a relative optimum: further interactive tuning of tracking parameters does no longer promise to improve the overall system performance significantly. Even the incorporation of further knowledge regarding vehicle and scene geometry or illumination has to cope with an increasing level of interaction between different knowledge sources and system parameters. Our results indicate that model-based tracking of rigid objects in monocular image sequences may have to be reappraised more thoroughly than anticipated during the recent past.     

Moving objects detection employing iterative update of the background

Detection of objects from a video is one of the basic issues in computer vision study. It is obvious that moving objects detection is particularly important, since they are those to which one should pay attention in walking, running, or driving a car. This paper proposes a method of detecting moving objects from a video as foreground objects by inferring backgrounds frame by frame. The proposed method can cope with various changes of a scene including large dynamical change of a scene in a video taken by a stationary/moving camera. Experimental results show satisfactory performance of the proposed method.     

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     

视频序列的全景图拼接技术

提出了一种对视频序列进行全景图拼接的方法。主要讨论了有大面积的非刚性运动物体出现的序列，不过此方法也同样适用于无运动物体的纯背景序列。为计算各帧间的投影关系，用仿射模型来描述摄像机运动，并用特征点匹配的方法计算出模型中各参数的值。由于用相关法计算的匹配结果准确率比较低，所以用RANSAC（Random Sampling Consensus）对匹配结果进行了筛选，可以准确求出摄像机运动参数。利用运动参数进行投影，然后用多帧相减并求交集，估计出每帧图像中运动物体存在的区域，最后计算得到了全景图。该方法的结果与前人得到的结果进行了比较，证明用此方法能获得质量较高的全景图。     

Application of differential geometry to recognize and locate partially occluded objects

A new method has been proposed to recognize and locate partially occluded two-dimensional rigid objects of a given scene. For this purpose we initially generate a set of local features of the shapes using the concept of differential geometry. Finally a computer vision scheme, based upon matching local features of the objects in a scene and the models which are considered as cognitive database, is described using hypothesis generation and verification of features for the best possible recognition.     

Detecting moving objects using the rigidity constraint

A method for visually detecting moving objects from a moving camera using point correspondences in two orthographic views is described. The method applies a simple structure-from-motion analysis and then identifies those points inconsistent with the interpretation of the scene as a single rigid object. It is effective even when the actual motion parameters cannot be recovered. Demonstrations are presented using point correspondences automatically determined from real image sequences     

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.