一种复杂场景下的运动目标跟踪算法

提出了一种基于跟踪窗口自适应和抗遮挡的目标跟踪算法。采用Mean Shift算法确定当前帧的目标位置,最优选取核函数带宽,使跟踪窗口能够根据目标尺寸大小作出自适应调整。利用Bhattacharyya系数作为遮挡的判断依据,当目标遮挡时引入卡尔曼滤波器估计目标的运动信息,进行后续状态预测。实验表明,该算法能有效跟踪复杂场景下的运动目标。     

基于残差分布的H.264自适应整帧恢复算法

针对H.264在网络传输中丢包造成整帧丢失的问题,提出了一种基于残差分布的H.264自适应整帧恢复算法。首先对参考帧的残差信息进行分析,对于平坦区域或刚性运动区域,采用传统的运动矢量复制法,而对运动复杂区域或产生形变等区域进行基于像素的运动矢量重新估计,再以光流法进行恢复。实验结果表明,该算法在主观视觉质量和客观峰值信噪比(PSNR)上都优于传统方法。     

一种改进的Camshift/Kalman运动目标跟踪算法

针对目标跟踪中的目标遮挡、丢失等情况,提出了一种改进的基于空间边缘方向直方图的Camshfit/Kalman跟踪算法.首先,利用空间边缘梯度方向作为匹配信息,同时自适应修正每帧中的匹配模板,再使用Kalman滤波器对运动目标的位置进行预测更新,以克服目标遮挡情况及噪声的干扰.实验表明,该算法能够较好处理目标遮挡情况,实现运动目标的高精度跟踪.     

港口船舶位置目标图像跟踪监测研究

由于船舶目标成像小,江面干扰噪声大,船舶遮挡及连续多帧丢失等因素影响监测精度,在图像连续帧间创建位置、速度、面积等匹配方程进行跟踪存在匹配问题。为解决上述问题,提出一种多结构Top-hat变换和改进的SVD-UKF算法进行检测跟踪。通过分析船舶几何特征来设计多结构元素Top-hat变换检测船舶;在改进的SVD-UKF基础上定义自适应阈值和路径连贯性函数匹配方程进行跟踪以提高监测精度。实验结果证明,该算法可提高监测精度,跟踪稳定,并解决了船舶遮挡及连续多帧船舶丢失等问题,满足实时性需求。     

基于Mean Shift的多模板目标跟踪算法

目标跟踪是计算机视觉研究领域的热点之一,并得到广泛应用。其中基于Mean Shift的运动目标跟踪算法因其计算量小,实时性好,简单易行等特点而受到广泛关注,但该算法在目标突变或严重帧丢失以及目标严重遮挡的情况跟踪效果不佳,留下了改进空间。在传统基于Mean Shift运动目标跟踪方法基础上,通过创建并维护多样性模板库为跟踪过程提供更丰富的目标描述信息,提高算法运动目标跟踪效果。实验结果表明,新算法较好地解决了在目标突变和严重帧丢失情况下不能准确跟踪目标的问题,并且对目标的完全遮挡也具有很好的鲁棒性。     

基于改进Mean-Shift与自适应Kalman滤波的视频目标跟踪

提出一种改进的Mean-Shift和自适应Kalman滤波器相结合的视频运动目标跟踪算法。对选定的跟踪目标,采用三帧差和区域增长法分割目标并得到主颜色信息。在跟踪过程中,利用自适应的Kalman滤波器估计每一帧的起始迭代位置,再利用改进的Mean-Shift算法得到跟踪位置并作为测量值反馈给自适应Kalman滤波器,并引入遮挡率因子以自适应地调节Kalman估计参数。实验结果表明,该算法能对视频中的运动目标实现检测和连续跟踪,对遮挡也有较好的鲁棒性。     

适用于遮挡问题的目标跟踪算法

提出一种基于网格模型的目标跟踪算法．该算法首先进行遮挡区域检测，然后进行网格结点的运动估计和网格更新过程完成目标的多帧跟踪．改进的遮挡区域检测算法有效地提高了检测准确度，从而确保遮挡区域的准确跟踪；网格结点的运动估计是通过特征窗口运动补偿匹配完成，可以有效地克服块效应．实验证明，该算法解决了二维运动估计时网格模型在遮挡区域存在的问题，并可以有效地进行目标准确跟踪．     

基于多子块联合估计的相关滤波跟踪

针对遮挡情况下相关滤波算法跟踪精度下降的问题,提出了一种基于多子块联合估计的核相关滤波跟踪方法。首先依据初始帧跟踪框的几何特征对目标自适应分块,并采用KCF方法对各子块独立跟踪得到联合置信图;然后以上帧目标的位置及尺度作为先验信息对搜索区域采样,同时将样本框中置信图的权值密度作为观测值,利用粒子滤波算法实现候选目标的最优估计;最后对置信度较低的子块反向投影至上帧图像进行遮挡检测,防止模板错误更新。定性和定量实验结果表明,该方法与原始KCF算法相比跟踪精度提升约10%,具有良好的抗遮挡性,并对目标尺度变化具有一定的估计能力。     

基于复小波和支持向量机的纹理分类法*

针对图像纹理分类问题,提出了一种将二元树复小波变换与支持向量机相结合的分类方法,通过二元树复小波变换对纹理图像进行四层分解,提取各子频带小波系数模的均值和标准方差组成特征向量,利用支持向量机作为分类器实现纹理图像分类。对20类Brodatz纹理图像的分类实验表明,提出的方法具有较高的分类精度,在有限训练样本的情况下比传统的分类算法平均正确率有10%左右的提高,体现了该方法的有效性和良好的泛化能力。     

基于非运动区域划分的运动估计算法

针对利用角点进行运动估计时,存在一定数量的运动区域角点,导致估计速度慢、精度低的问题,提出了一种基于非运动区域划分的运动估计算法。算法采用栅格法划分参考帧图像上的非运动区域与运动区域;对参考帧图像上提取的角点进行筛选,利用非运动区域的角点与当前帧图像上角点进行匹配;根据距离不变准则去除误匹配后,估算运动矢量。实验结果表明,该算法有效地提高了运行效率和估计精度。     

Motion from occlusions

In computer vision, occlusions are almost always seen as undesirable singularities that pose difficult challenges to image motion analysis problems, such as optic flow computation, motion segmentation, disparity estimation, or egomotion estimation. However, it is well known that occlusions are extremely powerful cues for depth or motion perception, and could be used to improve those methods.

In this paper, we propose to recover camera motion information based uniquely on occlusions, by observing two specially useful properties: occlusions are independent of the camera rotation, and reveal direct information about the camera translation.

We assume a monocular observer, undergoing general rotational and translational motion in a static environment. We present a formal model for occlusion points and develop a method suitable for occlusion detection. Through the classification and analysis of the detected occlusion points, we show how to retrieve information about the camera translation (FOE). Experiments with real images are presented and discussed in the paper.     

基于内容的遮挡网格生成算法

基于目标的网格模型可以有效消除传统块匹配法进行运动补偿时产生的块效应，但是网格增强的帧内联接关系不适用于遮挡区域。本文在准确检测遮挡区域的前提下，进行遮挡的网格设计，即在遮挡区域不放置网格节点以及节点的运动矢量不进入遮挡区域，有效地解决了遮挡区域的运动估计问题。同时，利用鲁棒性较好的SUSAN检测算子进行特征点提取，建立基于内容的网格模型。实验证明，该算法是可行的，并具有良好的视觉效果。     

Multimodal estimation of discontinuous optical flow using Markovrandom fields

The estimation of dense velocity fields from image sequences is basically an ill-posed problem, primarily because the data only partially constrain the solution. It is rendered especially difficult by the presence of motion boundaries and occlusion regions which are not taken into account by standard regularization approaches. In this paper, the authors present a multimodal approach to the problem of motion estimation in which the computation of visual motion is based on several complementary constraints. It is shown that multiple constraints can provide more accurate flow estimation in a wide range of circumstances. The theoretical framework relies on Bayesian estimation associated with global statistical models, namely, Markov random fields. The constraints introduced here aim to address the following issues: optical flow estimation while preserving motion boundaries, processing of occlusion regions, fusion between gradient and feature-based motion constraint equations. Deterministic relaxation algorithms are used to merge information and to provide a solution to the maximum a posteriori estimation of the unknown dense motion field. The algorithm is well suited to a multiresolution implementation which brings an appreciable speed-up as well as a significant improvement of estimation when large displacements are present in the scene. Experiments on synthetic and real world image sequences are reported     

基于遮挡区域建模和目标运动估计的动态遮挡规避方法

对于运动视觉目标，如何对遮挡区域进行规避是视觉领域一个具有挑战性的问题.本文提出了一种新颖的基于运动视觉目标深度图像利用遮挡信息实现动态遮挡规避的方法.该方法主要利用遮挡区域最佳观测方位模型和视觉目标运动估计方程，通过合理规划摄像机的观测方位逐渐完成对遮挡区域的观测.主要贡献在于:1）提出了深度图像遮挡边界中关键点的概念，利用其构建关键线段对遮挡区域进行快速建模；2）基于关键线段和遮挡区域建模结果，提出了一种构建遮挡区域最佳观测方位模型的方法；3）提出一种混合曲率特征，通过计算深度图像对应的混合曲率矩阵，增加了图像匹配过程中提取特征点的数量，有利于准确估计视觉目标的运动.实验结果验证了所提方法的可行性和有效性.     

2D human body tracking with Structural Kalman filter

Tracking moving objects is one of the most important techniques in motion analysis and understanding, and it has many difficult problems to solve. Especially, estimating and identifying moving objects, when the background and moving objects vary dynamically, are very difficult. It is possible under such a complex environment that targets may disappear totally or partially due to occlusion by other objects. The Kalman filter has been used to estimate motion information and use the information in predicting the appearance of targets in the succeeding frames. In this paper, we propose another version of the Kalman filter, to be called Structural Kalman filter, which can successfully work its role of estimating motion information under such a deteriorating condition as occlusion. Experimental results show that the suggested approach is very effective in estimating and tracking non-rigid moving objects reliably.     

Tracking a partially occluded target with a cluster of Kalman filters

Tracking moving objects is one of the most important techniques in motion analysis and understanding, and it has many difficult problems to solve. Estimating and identifying moving objects, when the background and moving objects vary dynamically, are especially difficult. It is possible under such a complex environment that targets might disappear totally or partially due to occlusion by other objects. The Kalman filter has been used to estimate motion information and use the information in predicting the appearance of targets in succeeding frames. In this article, we propose another version of the Kalman filter, to be called the structural Kalman filter, which can successfully accomplish its role of estimating motion information under such a deteriorating condition as occlusion. Experimental results show that the suggested approach is very effective in estimating and tracking non‐rigid moving objects reliably. © 2002 Wiley Periodicals, Inc.     

外极面图像的运动遮挡模型和运动纹理方向检测算法

运动遮挡边界处的运动估计是一种困难的问题,外极面图像方法将运动估计转化为转迹线的检测,人造物体的轨迹线容易通过边缘跟踪的方法获得,但对于纹理复杂的自然景物,轨迹跟踪较为困难。     

基于深度学习的光流插帧边信息生成算法

在分布式视频编码（DVC）中,如何在各种运动场景下生成高质量的边信息并提升解码性能是一个重点研究领域。提出一种基于深度学习的光流插帧边信息生成算法（optical flow interpolation,OFI）,编码端采用区间重叠的分布式算数码（distributed arithmetic coding,DAC）对视频进行编码,解码端生成边信息时提取已解码关键帧,输入深度学习光流插帧网络。网络采用多层光流模块产生光流,并结合光流向后弯曲关键帧产生初步的边信息估计,再由融合过程消除遮挡产生更加细化的结果,最后边信息辅助解码树完成解码。实验结果表明,与现有方法相比,该方法PSNR最大可提升2.25 dB,主要体现在线性运动场景下。同时在线性和非线性场景下SSIM指标可提升0.001 5~0.064 8,在解码视频率失真曲线上也体现出一致的结果,证明了该算法对线性运动边信息估计有较好的提升,对非线性运动边信息结构也有良好的恢复性。     

Oriented structure of the occlusion distortion: is it reliable?

In the energy spectrum of an occlusion sequence, the distortion term has the same orientation as the velocity of the occluding signal. Other works claimed that this oriented structure can be used to distinguish the occluding velocity from the occluded one. We argue that the orientation structure of the distortion cannot always work as a reliable feature due to the rapidly decreasing energy contribution. This already weak orientation structure is further blurred by a superposition of distinct distortion components. We also indicate that the superposition principle of Shizawa and Mase (1991) for multiple motion estimation needs to be adjusted