基于Snake模型的视频对象分割和跟踪算法

视频对象的分割是基于内容的视频处理中重要的组成部分。提出并实现了一种半自动视频对象分割和跟踪算法。算法主要基于Williams活动轮廓模型，通过求取轮廓点的局部能量最小值对轮廓线进行更新。轮廓扩张技术用来追踪变形的轮廓边缘。通过对轮廓中心点运动的统计，预测对象的运动方向和大小。实验仿真结果表明，这种改进的Snake算法能够收缩到图像的凹陷部分，而且能较好地跟踪视频对象的运动。     

Image segmentation using a multilayer level-set approach

We propose an efficient multilayer segmentation method based on implicit curve evolution and on variational approach. The proposed formulation uses the minimal partition problem as formulated by D. Mumford and J. Shah, and can be seen as a more efficient extension of the segmentation models previously proposed in Chan and Vese (Scale-Space Theories in Computer Vision, Lecture Notes in Computer Science, Vol. 1682, pp. 141–151, 1999, IEEE Trans Image Process 10(2):266–277, 2001), and Vese and Chan (Int J Comput Vis 50(3):271–293, 2002). The set of unknown discontinuities is represented implicitly by several nested level lines of the same function, as inspired from prior work on island dynamics for epitaxial growth (Caflisch et al. in Appl Math Lett 12(4):13, 1999; Chen et al. in J Comput Phys 167:475, 2001). We present the Euler–Lagrange equations of the proposed minimizations together with theoretical results of energy decrease, existence of minimizers and approximations. We also discuss the choice of the curve regularization and conclude with several experimental results and comparisons for piecewise-constant segmentation of gray-level and color images.     

Integrating tracking with fine object segmentation

We present a novel method for on-line, joint object tracking and segmentation in a monocular video captured by a possibly moving camera. Our goal is to integrate tracking and fine segmentation of a single, previously unseen, potentially non-rigid object of unconstrained appearance, given its segmentation in the first frame of an image sequence as the only prior information. To this end, we tightly couple an existing kernel-based object tracking method with Random Walker-based image segmentation. Bayesian inference mediates between tracking and segmentation, enabling effective data fusion of pixel-wise spatial and color visual cues. The fine segmentation of an object at a certain frame provides tracking with reliable initialization for the next frame, closing the loop between the two building blocks of the proposed framework. The effectiveness of the proposed methodology is evaluated experimentally by comparing it to a large collection of state of the art tracking and video-based object segmentation methods on the basis of a data set consisting of several challenging image sequences for which ground truth data is available.     

基于自适应分块的视频目标跟踪方法研究*

针对目标跟踪中的遮挡问题,提出了一种基于颜色特征的自适应跟踪算法。该算法利用模糊K均值聚类将目标自适应分块,采用单调递减的核函数对子块进行加权,目标模型的更新只需更新子块的颜色均值,计算量小。在跟踪过程采用目标整体匹配的方法,结合设计的自适应Kalman滤波器,有效地解决了跟踪过程中的目标遮挡问题。实验结果表明,新算法能够实现目标的准确跟踪。     

Across-camera object tracking using a conditional random field model

The Journal of Supercomputing - To ensure safety, most public spaces now deploy monitoring systems. However, in most scenarios, the tracking operations of these monitoring systems are performed...     

Robust object tracking using a sparse coadjutant observation model

Multimedia Tools and Applications - This paper develops a classical visual tracker that is called a discriminative sparse similarity (DSS) tracker. Based on the classical Laplacian multi-task...     

一种基于小波变换的马尔可夫随机场的视频对象分割

马尔可夫随机场（Markov Random Field,MRF）理论已经被广泛地应用于视频图像的分割。提出一种基于小波变换的马尔可夫随机场模型的视频对象分割算法。该算法利用小波变换将图像序列分解到小波域,并在此基础上建立马尔可夫随机场模型,构造相应的能量函数。通过迭代求解能量函数的最优解,得出标记场,提取出运动对象。仿真结果表明,该算法能够有效地抑制噪声,提高构成对象边界像素的数量,快速有效地提取出视频对象。     

Spatiotemporal vehicle tracking: the use of unsupervised learning-based segmentation and object tracking

In this paper, a framework for spatiotemporal vehicle tracking using unsupervised learning-based segmentation and object tracking is presented. An adaptive background learning and subtraction method is proposed and applied to two real-traffic video sequences to obtain more accurate spatiotemporal information on the vehicle objects. As demonstrated in the experiments, almost all vehicle objects are successfully identified through this framework.     

Neuro-probabilistic model for object tracking

Pattern Analysis and Applications - Occlusion is one of the major challenges for object tracking in real-life scenario. Various techniques in particle filter framework have been developed to solve...     

3D object segmentation using B-Surface

3D object segmentation is important in computer vision such as target detection in biomedical image analysis. A new method, called B-Surface algorithm, is generated for 3D object segmentation. An improved 3D external force field combined with the normalized GVF is utilized. After the initialization of a surface model near the target, B-Surface starts to deform to locate the boundary of the object. First, it overcomes the difficulty that comes from analyzing 3D volume image slice by slice. And the speed of B-Surface deformation is enhanced since the internal forces are not needed to compute in every iteration deformation step. Next, the normal at every surface point can be calculated easily since B-Surface is a continuous deformable model. And it has the ability to achieve high compression ratio (ratio of data to parameters) by presenting the whole surface with only a relatively small number of control points. Experimental results and analysis are presented in this paper. We can see that the B-Surface algorithm can find the surface of the target efficiently.     

An active contour model for object tracking using the previous contour

An active contour model is proposed for object tracking using prior information. Conventional algorithms have many problems when applied in object tracking. The proposed active contour algorithm, a model using an edge of an adapted color feature, not only modifies the internal energy function of the conventional algorithm to extend the search range and reduce the computational burden, but also modifies the external energy function to reduce the edge candidates of the object. The algorithm searches normally and uses dynamic programming to solve the energy minimization problem. The main drawbacks of a conventional snake algorithm, i.e., shrinking, a limited search range, sensitivity to outliers, are improved with the proposed algorithm. We illustrate the effectiveness of our scheme using some tracking examples. This work was presented, in part, at the Seventh International Symposium on Artificial Life and Robotics, Oita, Japan, January 16–18, 2002     

目标跟踪中的四叉树分块模型

针对计算机视觉领域中,建立能够描述图像结构特性的目标模型问题,提出了一种基于四叉树分块的密度估计模型。采用四叉树分块方法对目标图像块进行划分,根据各分块的尺度和像素值统计特性建立局部的密度估计模型。实验结果和分析表明,基于四叉树分块的局部密度估计模型解决了全局密度估计模型无法描述结构特性的问题,而且用较少的分块数量有针对性地描述了目标的细节,同时简化了密度估计算法的计算过程,在目标跟踪应用中较其他算法表现出较优的性能。     

Highly non-rigid video object tracking using segment-based object candidates

Multimedia Tools and Applications - A novel scheme for non-rigid video object tracking using segment-based object candidates is proposed in this paper. Rather than using a conventional bounding...     

Moving object detection using region tracking

For many vision-based systems, it is important to detect a moving object automatically. The region-based motion estimation method is popular for automatic moving object detection. The region-based method has several advantages in that it is robust to noise and variations in illumination. However, there is a critical problem in that there exists an occlusion problem which is caused by the movement of the object. The occlusion problem results in an incorrect motion estimation and faulty detection of moving objects. When there are occlusion regions, the motion vector is not correctly estimated. That is, a stationary background in the occluded region can be classified as a moving object.In order to overcome this occlusion problem, a new occlusion detection algorithm is proposed. The proposed occlusion detection algorithm is motivated by the assumption that the distribution of the error histogram of the occlusion region is different from that of the nonocclusion region. The proposed algorithm uses the mean and variance values to decide whether an occlusion has occurred in the region. Therefore, the proposed occlusion detection and motion estimation scheme detects the moving regions and estimates the new motion vector, while avoiding misdetection caused by the occlusion problem. The experimental results for several video sequences demonstrate the robustness of the proposed approach to the occlusion problem.This work was presented in part at the 8th International Symposium on Artificial Life and Robotics, Oita, Japan, January 24–26, 2003     

Efficient liver segmentation using a level-set method with optimal detection of the initial liver boundary from level-set speed images

Automatic liver segmentation is difficult because of the wide range of human variations in the shapes of the liver. In addition, nearby organs and tissues have similar intensity distributions to the liver, making the liver's boundaries ambiguous. In this study, we propose a fast and accurate liver segmentation method from contrast-enhanced computed tomography (CT) images. We apply the two-step seeded region growing (SRG) onto level-set speed images to define an approximate initial liver boundary. The first SRG efficiently divides a CT image into a set of discrete objects based on the gradient information and connectivity. The second SRG detects the objects belonging to the liver based on a 2.5-dimensional shape propagation, which models the segmented liver boundary of the slice immediately above or below the current slice by points being narrow-band, or local maxima of distance from the boundary. With such optimal estimation of the initial liver boundary, our method decreases the computation time by minimizing level-set propagation, which converges at the optimal position within a fixed iteration number. We utilize level-set speed images that have been generally used for level-set propagation to detect the initial liver boundary with the additional help of computationally inexpensive steps, which improves computational efficiency. Finally, a rolling ball algorithm is applied to refine the liver boundary more accurately. Our method was validated on 20 sets of abdominal CT scans and the results were compared with the manually segmented result. The average absolute volume error was 1.25+/-0.70%. The average processing time for segmenting one slice was 3.35 s, which is over 15 times faster than manual segmentation or the previously proposed technique. Our method could be used for liver transplantation planning, which requires a fast and accurate measurement of liver volume.     

3-D object segmentation using ant colonies

3-D object segmentation is an important and challenging topic in computer vision that could be tackled with artificial life models.A Channeler Ant Model (CAM), based on the natural ant capabilities of dealing with 3-D environments through self-organization and emergent behaviours, is proposed.Ant colonies, defined in terms of moving, pheromone laying, reproduction, death and deviating behaviours rules, is able to segment artificially generated objects of different shape, intensity, background.The model depends on few parameters and provides an elegant solution for the segmentation of 3-D structures in noisy environments with unknown range of image intensities: even when there is a partial overlap between the intensity and noise range, it provides a complete segmentation with negligible contamination (i.e., fraction of segmented voxels that do not belong to the object). The CAM is already in use for the automated detection of nodules in lung Computed Tomographies.     

Color image segmentation using competitive learning

Presents a color image segmentation method which divides the color space into clusters. Competitive learning is used as a tool for clustering the color space based on the least sum-of-squares criterion. We show that competitive learning converges to approximate the optimum solution based on this criterion, theoretically and experimentally. We apply this method to various color scenes and show its efficiency as a color image segmentation method. We also show the effects of using different color coordinates to be clustered, with some experimental results     

A graphical model for audiovisual object tracking

We present a new approach to modeling and processing multimedia data. This approach is based on graphical models that combine audio and video variables. We demonstrate it by developing a new algorithm for tracking a moving object in a cluttered, noisy scene using two microphones and a camera. Our model uses unobserved variables to describe the data in terms of the process that generates them. It is therefore able to capture and exploit the statistical structure of the audio and video data separately, as well as their mutual dependencies. Model parameters are learned from data via an EM algorithm, and automatic calibration is performed as part of this procedure. Tracking is done by Bayesian inference of the object location from data. We demonstrate successful performance on multimedia clips captured in real world scenarios using off-the-shelf equipment.     

Pearson-based mixture model for color object tracking

To track objects in video sequences, many studies have been done to characterize the target with respect to its color distribution. Most often, the Gaussian mixture model (GMM) is used to represent the object color density. In this paper, we propose to extend the normality assumption to more general families of distributions issued from the Pearson’s system. Precisely, we propose a method called Pearson mixture model (PMM), used in conjunction with Gaussian copula, which is dynamically updated to adapt itself to the appearance change of the object during the sequence. This model is combined with Kalman filtering to predict the position of the object in the next frame. Experimental results on gray-level and color video sequences show tracking improvements compared to classical GMM. Especially, the PMM seems robust to illumination variations, pose and scale changes, and also to partial occlusions, but its computing time is higher than the computing time of GMM.     

Robust object tracking using least absolute deviation

Recently, sparse representation has been applied to object tracking, where each candidate target is approximately represented as a sparse linear combination of target templates. In this paper, we present a new tracking algorithm, which is faster and more robust than other tracking algorithms, based on sparse representation. First, with an analysis of many typical tracking examples with various degrees of corruption, we model the corruption as a Laplacian distribution. Then, a LAD–Lasso optimisation model is proposed based on Bayesian Maximum A Posteriori (MAP) estimation theory. Compared with L1 Tracker and APG-L1 Tracker, the number of optimisation variables is reduced greatly; it is equal to the number of target templates, regardless of the dimensions of the feature. Finally, we use the Alternating Direction Method of Multipliers (ADMM) to solve the proposed optimisation problem. Experiments on some challenging sequences demonstrate that our proposed method performs better than the state-of-the-art methods in terms of accuracy and robustness.