基于运动显著性的移动镜头下的运动目标检测

针对移动镜头下的运动目标检测中的背景建模复杂、计算量大等问题,提出一种基于运动显著性的移动镜头下的运动目标检测方法,在避免复杂的背景建模的同时实现准确的运动目标检测。该方法通过模拟人类视觉系统的注意机制,分析相机平动时场景中背景和前景的运动特点,计算视频场景的显著性,实现动态场景中运动目标检测。首先,采用光流法提取目标的运动特征,用二维高斯卷积方法抑制背景的运动纹理;然后采用直方图统计衡量运动特征的全局显著性,根据得到的运动显著图提取前景与背景的颜色信息;最后,结合贝叶斯方法对运动显著图进行处理,得到显著运动目标。通用数据库视频上的实验结果表明,所提方法能够在抑制背景运动噪声的同时,突出并准确地检测出场景中的运动目标。     

On clustering and retrieval of video shots through temporal slices analysis

Based on the analysis of temporal slices, we propose novel approaches for clustering and retrieval of video shots. Temporal slices are a set of two-dimensional (2-D) images extracted along the time dimension of an image volume. They encode rich set of visual patterns for similarity measure. In this paper, we first demonstrate that tensor histogram features extracted from temporal slices are suitable for motion retrieval. Subsequently, we integrate both tensor and color histograms for constructing a two-level hierarchical clustering structure. Each cluster in the top level contains shots with similar color while each cluster in bottom level consists of shots with similar motion. The constructed structure is then used for the cluster-based retrieval. The proposed approaches are found to be useful particularly for sports games, where motion and color are important visual cues when searching and browsing the desired video shots.     

Semantic Plane-Structure based motion detection with a nonstationary camera

This paper presents a novel method to accurately detect moving objects from a video sequence captured using a nonstationary camera. Although common methods provide effective motion detection for static backgrounds or through only planar-perspective transformation, many detection errors occur when the background contains complex dynamic interferences or the camera undergoes unknown motions. To solve this problem, this study proposed a motion detection method that incorporates temporal motion and spatial structure. In the proposed method, first, spatial semantic planes are segmented, and image registration based on stable background planes is applied to overcome the interferences of the foreground and dynamic background. Thus, the estimated dense temporal motion ensures that small moving objects are not missed. Second, motion pixels are mapped on semantic planes, and then, the spatial distribution constraints of motion pixels, regional shapes and plane semantics, which are integrated into a planar structure, are used to minimise false positives. Finally, based on the dense temporal motion and spatial structure, moving objects are accurately detected. The experimental results on CDnet dataset, Pbi dataset, Aeroscapes dataset, and other challenging self-captured videos under difficult conditions, such as fast camera movement, large zoom variation, video jitters, and dynamic background, revealed that the proposed method can remove background movements, dynamic interferences, and marginal noises and can effectively obtain complete moving objects.© 2017 ElsevierInc.Allrightsreserved.     

On the detection of motion and the computation of optical flow

A method for the detection of motion in image sequences is presented. In this method, the intensity history at each pixel is convolved with the second derivative in time of a temporal Gaussian smoothing function. The zero crossings in a single frame of the resulting function indicate the positions of moving edges. Intensity changes in time due to illumination effects do not produce zero crossings; thus, they are not interpreted as motion by the present method. It is also shown that the spatial and temporal derivatives of this function can be used to compute the component of the optical flow that is normal to the zero-crossing contours. This computation is also insensitive to nonconvective temporal and spatial variations in the image intensity that are caused by illumination effects     

基于时空分析的视频前景提取

为了从包含动态背景或者非平移运动前景的视频中提取完整的前景区域,提出一种视频分割算法。首先,将视频中单个像素的变化过程视为离散时间信号,运用时间轴的Gabor滤波对时域信息进行分析,将视频粗分为前景和背景;然后,运用均值漂移算法对前景和背景做颜色聚类分析,分析空域的颜色关联信息,分别建立全局颜色模型和局部颜色模型;最后,运用双重标记法提取视频前景。该算法综合考虑视频的时域信息和空域信息。在多个视频库的测试结果表明,该算法可以显著提高前景区域提取的精度,特别是对于背景动态变化或者前景发生非平移运动的视频。     

一种压缩域中的快速运动目标提取算法

论文提出了一种工作于MPEG压缩域的快速运动目标提取算法。算法以通过部分解码得到的运动向量和亮度分量的直流DCT系数作为输入,提取P帧的运动目标。首先采用鲁棒性回归分析估计全局运动,标记出与全局运动不一致的宏块,得到运动块的分布;然后将运动向量场插值作为时间域的特征,将重构的直流图像转换到LUV颜色空间作为空间域的特征,采用快速平均移聚类找到时间和空间特征具有相似性的区域,得到细化的区域边界;最后结合运动块分布和聚类分析的结果,通过基于马尔可夫随机场的统计标号方法进行背景分离,得到运动目标的掩模。实验结果表明该算法可以有效地消除运动向量噪声的影响,并有很高的处理速度,对于CIF格式的视频码流,每秒可以处理约50帧。     

Extracting mobile objects by sequential background detection on a video

This paper describes a technique for extracting moving objects from a video image sequence taken by a slowly moving camera as well as a fixed camera. The background subtraction method is effective for extracting moving objects from a video. But the latest background image should be employed for the subtraction in the mobile camera case and in order not to be influenced by the light intensity change. A temporal median technique is proposed in this paper which detects the background at every moment. The camera motion is estimated using a local correlation map and the temporal median filter is applied to the common image area among a set of successive image frames to extract the background. The technique was applied to the video images obtained at a junction from a hand-held camera and those taken at a pedestrians crossing by a camera fixed in a car and successfully detected pedestrians. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

基于运动信息与马尔可夫彩色聚类的运动目标分割

针对视频序列图像中的运动目标分割,论文提出了将运动检测和马尔可夫彩色聚类相结合的运动目标分割算法。该算法首先利用基于统计模型的运动检测算法,通过后处理,得到运动目标的初始模板。然后,利用区域生长算法进行彩色图像的初始分割,在初始分割的基础上应用马尔可夫随机场模型进行彩色聚类,得到具有精确边缘的分割区域。最后,将运动目标的初始模板和彩色精确分割结合起来提取出具有精确边缘的运动目标。实验结果表明该算法能有效地分割和提取出视频序列中的运动目标。     

一种时空联合的视频运动目标提取与跟踪新算法

提出了一种时空联合的视频运动目标提取与跟踪新算法。在空域分割中,针对分水岭方法过分割现象明显的缺点,对分水岭分割方法进行了改进;在时域分割中,首先对全局运动进行了补偿,随后为消除仅用两帧帧差进行对象分割所带来的误差,采用多帧帧差求和的方法,并自适应选取累积帧差的二值化阈值;时空分割结果进行投影融合后得到视频对象,提出用一种基于区域子块匹配的方法跟踪视频对象。实验结果表明,该算法简洁有效,能较好地把对象从运动背景中提取出来,并实现跟踪。     

三维场景建模的全景外极面图像分析方法

文章提出由非精确摄像机运动下的图像序列建立3D环境模型的全景外极面图像方法,实现了 无特征提取的时空纹理方向精确估计、深度边界确定和遮挡恢复算法.该方法推广并结合了 外极面图像方法和全景图像方法,避免了现有运动分层方法迭代过程中的局部最小化问题,具 有计算和存储效率高、适应性强、算法鲁棒性好的优点.建立的自然景物的真实感三维环境 模型,可用于机器人全局定位的自然路标提取和真实环境虚拟再现的图像合成.     

基于图像型森林火灾无线远程监控系统

针对目前我国森林火情远程监控中长时间不间断监测、运行成本过高等问题,提出了一种基于Linux系统和AT91RM9200处理器为平台的森林火灾监控系统。嵌入式处理器通过安装在森林监测点摄像头获取图像信息,采用图像增强、颜色聚类等图像处理手段,提取图像的颜色信息及其闪烁频率特性,运用火情识别算法判断视频图像帧是否含有火灾火焰,如果系统判断火灾发生,将火情信息通过无线网络传送到监控中心,交由上位机的监测人员做进一步处理。     

Shot Partitioning Based Recognition of TV Commercials

Digital video applications exploit the intrinsic structure of video sequences. In order to obtain and represent this structure for video annotation and indexing tasks, the main initial step is automatic shot partitioning. This paper analyzes the problem of automatic TV commercials recognition, and a new algorithm for scene break detection is then introduced. The structure of each commercial is represented by the set of its key-frames, which are automatically extracted from the video stream. The particular characteristics of commercials make commonly used shot boundary detection techniques obtain worse results than with other video content domains. These techniques are based on individual image features or visual cues, which show significant performance lacks when they are applied to complex video content domains like commercials. We present a new scene break detection algorithm based on the combined analysis of edge and color features. Local motion estimation is applied to each edge in a frame, and the continuity of the color around them is then checked in the following frame. By separately considering both sides of each edge, we rely on the continuous presence of the objects and/or the background of the scene during each shot. Experimental results show that this approach outperforms single feature algorithms in terms of precision and recall.     

海量监控视频快速回放与检索技术

为解决海量监控视频的快速浏览和检索,介绍了一种基于目标索引的视频摘要和检索方法。该方法在光流分析的基础上,在画面的静止区域更新背景,运动的区域利用差分法分割出运动目标图像。经过优化的快速特征匹配和建立运动跟踪模型后,根据目标运动轨迹,按照时空距离进行聚类。在目标图像数据和运动参数进行XML结构化存储为索引的基础上,最后在检索时将符合条件的所有目标图像,按照其原有时间顺序逐帧贴到同一个背景图像中,形成动态的摘要视频。由于该方法剔除了背景中大量的时空冗余信息,可在较短回放时间内浏览全部有用目标,显著提高海量监控视频的查阅效率。     

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。     

Video repairing under variable illumination using cyclic motions

This paper presents a complete system capable of synthesizing a large number of pixels that are missing due to occlusion or damage in an uncalibrated input video. These missing pixels may correspond to the static background or cyclic motions of the captured scene. Our system employs user-assisted video layer segmentation, while the main processing in video repair is fully automatic. The input video is first decomposed into the color and illumination videos. The necessary temporal consistency is maintained by tensor voting in the spatio-temporal domain. Missing colors and illumination of the background are synthesized by applying image repairing. Finally, the occluded motions are inferred by spatio-temporal alignment of collected samples at multiple scales. We experimented on our system with some difficult examples with variable illumination, where the capturing camera can be stationary or in motion.     

基于视频六维分布模型的运动对象分割

视频运动对象检测是智能安全监控的基本问题。考虑视频的时间空间分布规律，该文构造了视频六维分布模型以进行运动对象分割。在时间维上利用在线高斯混合模型对背景进行更新，并且计算像素颜色差异。在空间维上利用纹理特征描述像素点之间结构关系，提高了对光线变化和阴影等环境变化的抗干扰能力。实验结果表明该算法是实用有效的。     

基于SIFT特征匹配与K-均值聚类的运动目标检测

运动摄像机情况下的运动目标检测是视频监控中的难点和热点问题。为了能够有效地检测出运动目标,根据视频中背景与运动目标的速度不同这一特点,提出了一个基于尺寸不变特征变换(SIFT)和K-均值聚类的运动目标检测方法。首先提取视频中相邻两帧图像的SIFT特征点并进行匹配,并计算匹配特征点的运动速度,最后将运动目标和背景上的SIFT特征点K-均值聚类分析,在单运动目标、多运动目标和带有摄像头旋转情况下做了实验。实验结果表明,提出的目标检测算法能够在运动背景下较好地检测到目标并保留稳定的目标局部特征,对于摄像机运动、摄像机旋转、亮度变化等影响因素具有较强的适应能力。     

Detecting bipedal motion from correlated probabilistic trajectories

This paper is about detecting bipedal motion in video sequences by using point trajectories in a framework of classification. Given a number of point trajectories, we find a subset of points which are arising from feet in bipedal motion by analysing their spatio-temporal correlation in a pairwise fashion. To this end, we introduce probabilistic trajectories as our new features which associate each point over a sufficiently long time period in the presence of noise. They are extracted from directed acyclic graphs whose edges represent temporal point correspondences and are weighted with their matching probability in terms of appearance and location. The benefit of the new representation is that it practically tolerates inherent ambiguity for example due to occlusions. We then learn the correlation between the motion of two feet using the probabilistic trajectories in a decision forest classifier. The effectiveness of the algorithm is demonstrated in experiments on image sequences captured with a static camera, and extensions to deal with a moving camera are discussed.     

基于帧差和小波包分析算法的运动目标检测

提出了一种在镜头不动的情况下基于累积帧差分割和小波包分析融合技术的运动目标检测方法。这种方法可分为四步:使用改进的累积帧差算法和阈值分割算法完成目标区域的分割,并获得初始运动模板;利用小波包分析算法提取出单帧图像的边缘信息并获得细化的目标区域边缘图;根据初始运动模板和空域边缘图像的融合得到更精确的运动目标模板;最后结合原序列图像检测出完整的运动目标。实验结果表明:这种方法可以有效地从对比度较小和噪声较大的视频序列中较精确地检测出完整的运动目标。