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     

基于双向轮廓跟踪的面部轮廓线生成算法

为了提高医学图像中面部轮廓线的分割效果,提出一种新的面部轮廓线生成算法(bidirectional contour tracking,BCT).该算法首先利用边缘检测算法对CT图像进行检测,然后利用头部CT图像中各组织的灰度特征和图像的对称性,对检测后的图像进行双向轮廓跟踪,并获得面部的轮廓线.另外,根据面部轮廓线的特点,使用中点法可生成辅助轮廓线.以一组颅脑CT图像为例来验证算法的效果,实验结果表明,新的生成算法提高了轮廓线的生成速度,并有很好的分割效果.     

Efficient edge-based object tracking

Object tracking has been widely applied to video surveillance, robot localization and human-computer interaction. In this paper, an edge-based tracking algorithm is proposed. We extract the feature points by efficiently utilizing the image edges in the object region. Then the parameter vector of the object's motion model is estimated based on minimizing the sum-of-squared differences between the reference feature points in the reference frame and the observed feature points in the tracking sequence frame. The experiments show that the edge-based tracking algorithm proposed by us can track object efficiently under uniform and varying illumination conditions.     

Scalable continuous object detection and tracking in sensor networks

With the advancement of MEMS technologies, sensor networks have opened up broad application prospects. An important issue in wireless sensor networks is object detection and tracking, which typically involves two basic components, collaborative data processing and object location reporting. The former aims to have sensors collaborating in determining a concise digest of object location information, while the latter aims to transport a concise digest to sink in a timely manner. This issue has been intensively studied in individual objects, such as intruders. However, the characteristic of continuous objects has posed new challenges to this issue. Continuous objects can diffuse, increase in size, or split into multiple continuous objects, such as a noxious gas. In this paper, a scalable, topology-control-based approach for continuous object detection and tracking is proposed. Extensive simulations are conducted, which show a significant improvement over existing solutions.     

基于边缘直方图的粒子滤波跟踪算法及其实现

以颜色直方图为特征的运动目标跟踪算法容易受到光线变化及视场内其它同色目标的干扰.采用运动目标的边缘方向直方图作为特征,利用序列重要性采样原理和粒子滤波算法实现了对人体运动目标的跟踪.实验显示了该算法在光线变化及存在同色目标干扰时能够有效跟踪目标.在算法实现过程中,采用积分图计算边缘方向直方图,减少了计算时间,提高了计算速度,达到了实时跟踪的效果.     

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。     

联合轮廓法在低分辨率视频下的多目标追踪

相似目标间的遮挡一直是多目标追踪问题中的难点。针对这个问题提出了联合轮廓法。这种方法在使用粒子滤波器的基础上,通过对遮挡目标集的外轮廓建模来确定处于遮挡关系中目标的状态,从而进一步得到目标的轨迹。算法在实验中显示了良好的鲁棒性,并且计算复杂度与遮挡目标的个数成二次多项式关系。     

Structural similarity-based object tracking in multimodality surveillance videos

This paper addresses the problem of object tracking in video sequences for surveillance applications by using a recently proposed structural similarity-based image distance measure. Multimodality surveillance videos pose specific challenges to tracking algorithms, due to, for example, low or variable light conditions and the presence of spurious or camouflaged objects. These factors often cause undesired luminance and contrast variations in videos produced by infrared sensors (due to varying thermal conditions) and visible sensors (e.g., the object entering shadowy areas). Commonly used colour and edge histogram-based trackers often fail in such conditions. In contrast, the structural similarity measure reflects the distance between two video frames by jointly comparing their luminance, contrast and spatial characteristics and is sensitive to relative rather than absolute changes in the video frame. In this work, we show that the performance of a particle filter tracker is improved significantly when the structural similarity-based distance is applied instead of the conventional Bhattacharyya histogram-based distance. Extensive evaluation of the proposed algorithm is presented together with comparisons with colour, edge and mean-shift trackers using real-world surveillance video sequences from multimodal (infrared and visible) cameras.     

Improving the selection of feature points for tracking

The problem considered in this paper is how to select the feature points (in practice, small image patches are used) in an image from an image sequence, such that they can be tracked adequately further through the sequence. Usually, the tracking is performed by some sort of local search method looking for a similar patch in the next image in the sequence. Therefore, it would be useful if we could estimate the size of the convergence region for each image patch. There is a smaller chance of error when calculating the displacement for an image patch with a large convergence region than for an image patch with a small convergence region. Consequently, the size of the convergence region can be used as a proper goodness measure for a feature point. For the standard Kanade-Lucas-Tomasi (KLT) tracking method, we propose a simple and fast way to approximate the convergence region for an image patch. In the experimental part, we test our hypothesis on a large set of real data.     

基于轮廓分段特征描述的遮挡目标识别算法

针对传统目标识别算法对于遮挡目标识别准确率低的问题,提出了一种基于轮廓分段特征描述的遮挡目标识别算法。该算法首先采用离散曲线演化算法初步划分目标轮廓,根据分段起伏度进行分段优化,得到完整描述目标轮廓特征的有效分段;然后通过动态规划算法分析轮廓分段之间高度函数特征的相似度,利用特征显著度评价分段特征相对于目标整体特征的重要性;最后将分段之间的相似度和其特征显著度相结合,得到衡量识别准确率的联合相似度,获得最终的识别结果。通过对MPEG-7测试集进行实验分析,证明所提算法能够有效地对遮挡目标进行匹配识别,识别率优于常见的遮挡目标识别算法。     

基于形态学和活动轮廓模型的活体细胞图像分割算法

针对常用细胞轮廓分割方法人工干预较多的缺点,提出基于形态学和活动轮廓模型的活体细胞图像分割算法．利用形态学的基本操作提取活体细胞的初始轮廓,运用活动轮廓模型提取准确的细胞轮廓．实验表明该算法鲁棒性和准确性较好,能在无人工干预的情况下获取准确的细胞边缘．     

Spatial color histogram based center voting method for subsequent object tracking and segmentation

In this paper, we introduce an algorithm for object tracking in video sequences. In order to represent the object to be tracked, we propose a new spatial color histogram model which encodes both the color distribution and spatial information. Using this spatial color histogram model, a voting method based on the generalized Hough transform is employed to estimate the object location from frame to frame. The proposed voting based method, called the center voting method, requests every pixel near the previous object center to cast a vote for locating the new object center in the new frame. Once the location of the object is obtained, the back projection method is used to segment the object from the background. Experiment results show successful tracking of the object even when the object being tracked changes in size and shares similar color with the background.     

主动轮廓线舌体轮廓自动提取

主动轮廓线模型在分割图像时可以定位目标物体的边界,但是对初始点敏感和无法凹陷收敛等困难限制了该模型的推广.对梯度向量流模型研究后发现:该模型计算外部作用力场将图像的边界信息延展至离边界较远的图像区域,经过充分的迭代计算之后,边界信息可以覆盖大部分图像区域,这样就能解决模型固有的困难.舌体分割实验表明可以有效地解决该问题.     

基于边缘差分的运动目标检测方法

针对视频序列中的运动目标检测问题,提出了一种新的基于边缘差分的运动目标检测方法.通过改进的边缘检测算法提取视频序列中相邻帧的边缘图像并作差分,采用改进的Otsu法对差分图像进行阈值分割,得到运动目标检测的结果.结合Prewitt算子和Sobel算子改进的边缘检测算法能够获取纹理丰富、细节显著的边缘图像,边缘差分结果更加理想;改进的Otsu法联合类内方差能够很好地抑制噪声,保留更多的纹理细节.实验结果表明,提出的方法能够提取更加完整的目标区域,对背景噪声更加鲁棒.与最近一些同类算法相比,在背景运动和光照变化条件下,该方法具有更加优越的运动目标检测性能.     

一种基于联合轮廓特征矢量的目标识别方法

针对基于形状特征进行目标识别的方法存在的不足,提出一种联合轮廓不变矩特征和轮廓几何特征的识别方法;针对不同的待识别目标,采集足够数量的训练样本,统计每一个轮廓特征的均值、标准差和变异系数;并据此对这些轮廓特征进行动态筛选和加权,建立起待识别目标的联合轮廓特征矢量模型.在线识别时,提取场景目标的联合轮廓特征矢量,对其进行...     

基于聚合边缘方向直方图的图像检索算法

针对传统的边缘方向直方图在图像检索应用中的不足,提出了一种基于聚合边缘方向直方图的图像检索算法。采用小波模极大值边缘检测算法提取图像边缘;根据图像边缘点的方向信息及其之间的空间分布特征得到聚合边缘方向直方图;通过循环移位计算直方图间的最小距离作为相似度,实现了图像检索。该方法具有较好的平移、旋转、尺度不变性。实验结果证明了该算法的有效性和可行性,并取得了较好的检索效果。     

基于支持向量机的目标跟踪研究

把目标跟踪看作为目标和背景在时序上的分类问题来进行处理,选择支持向量机作为分类器,完成相应的目标跟踪任务。在跟踪过程中,采用扰动前景区域和按步长抽取背景的方法得到数量相当的正负样本;建立图像的积分直方图索引,通过索引之间的加减快速获取区域边缘和纹理特征向量;将新样本集合与前期获取的支持向量混合起来一并训练,实现分类器的在线更新。用经典图像序列进行实验,实验结果表明,该算法具有良好的跟踪效果。     

基于图像轮廓线方向特性的整体识别法

分析了传统矢量化方法所存在的一些问题,利用图像信息的几何特点,提出了一种基于图像轮廓线方向特性的整体识别法.由于引入了方向特性,使该算法较好地弥补了传统整体识别法在图段生成问题上的缺陷,使得图段信息更加单一化,识别效率更高,并给出了实验结果图,取得了较好的识别效果.