基于多特征信息融合粒子滤波的红外目标跟踪

多特征信息有较好的检测性能和适应性,而粒子滤波则是一种处理目标跟踪模型的非线性和非高斯特点的有效方法,将两者优点结合并针对红外图像特点,提出一种基于多特征信息融合的跟踪算法,该方法按一定的权值系数利用目标颜色和纹理特征构建模型,并融合于粒子滤波框架中。实验表明该跟踪方法能准确地跟踪海上红外运动目标。     

复杂背景下多信息融合的粒子滤波跟踪算法

将颜色和运动这两种信息融合进粒子滤波跟踪器,并提出分层抽样的方法,克服了利用单一信源所带来的跟踪不稳定问题;与典型的基于边缘特征或仅基于颜色信息的粒子滤波器相比,计算简单,并能够有效解决由于目标形状或颜色模糊而产生的跟踪困难问题.实验结果表明,该粒子滤波融合算法在复杂背景下能够稳健可靠的跟踪目标.     

基于多特征融合的粒子滤波目标跟踪研究

针对目标跟踪方法,本研究提出了一种基于运动特征和颜色特征多特征融合的粒子滤波跟踪方法,在颜色直方图描述颜色特征的基础上,融合了目标的运动特征,验证了通过增加对目标特征描述信息,可以提高跟踪健壮性以及可靠性。     

基于HOG和粒子滤波的足球视频中球的跟踪方法

在足球视频中,由于球员和摄像机的运动使得基于背景的方法无法有效检测和跟踪足球;足球在视频中所占像素个数少、运动复杂,并且在球场中有相似目标如球袜等的干扰,使得目标难以准确跟踪。为改善足球的跟踪效果,文中提出了一种基于梯度向量直方图和粒子滤波的足球跟踪算法。算法以粒子滤波为跟踪框架,用HOG特征与颜色特征融合,结合足球的结构信息与颜色信息,实现了足球视频中足球的稳定跟踪。实验结果表明,文中算法跟踪足球更准确,并可在存在相似目标干扰的条件下有效地跟踪足球。     

一种改进的粒子滤波和Mean Shift联合跟踪算法

为了提高视频运动目标跟踪的准确性和实时性,提出一种改进的粒子滤波和Mean Shift联合跟踪算法.针对传统粒子滤波跟踪算法中颜色直方图观测模型存在的局限性,提出了一种基于分块颜色直方图的观测模型描述方法,并根据该分块直方图的特点,重新设计了粒子权值的更新策略;针对粒子滤波算法实时性差的问题,提出了一种基于积分直方图的颜色特征快速计算方法,极大地降低了算法的运算量;为了降低相似背景干扰对跟踪效果的影响,提出了一种基于Gabor幅度谱的Mean Shift跟踪算法,并利用改进的Mean Shift算法对粒子滤波跟踪结果进行优化,提高了跟踪算法在复杂背景下的搜索能力.实验结果表明了算法的有效性.     

一种新型多特征融合粒子滤波视觉跟踪算法

针对单一视觉信息在动态变化环境下描述目标不够充分、跟踪目标不够稳定的缺点,提出了一种基于粒子滤波框架的新型多特征融合的视觉跟踪算法。采用颜色和形状信息来描述运动模型,通过民主合成策略将两种信息融合在一起,使得跟踪算法能根据当前跟踪形势自适应调整两种信息的权重以期达到最佳的最大似然比,实现信息间的优势互补。在设计粒子滤波跟踪算法时,利用自适应信息融合策略构建似然模型,提高了粒子滤波跟踪算法在复杂场景下的稳健性。实验结果表明,多特征融合跟踪算法不仅能准确、高效地跟踪目标,而且对光照、姿态变化引起的目标表观变化具有良好的鲁棒性。     

单站被动目标的多假设距离粒子滤波跟踪

针对单站被动目标跟踪的无距离信息,多假设距离粒子滤波方法利用多个粒子滤波的并行运算,对不同初始距离的粒子滤波器进行计算,利用各子滤波的残差概率对并行处理的粒子滤波运算进行自适应采样实现单站纯方位目标跟踪,并与粒子滤波、多假设距离滤波方法进行比较,仿真结果表明该算法收敛速度快,跟踪精度高。     

基于有限差分扩展卡尔曼粒子滤波的多特征自适应融合的目标跟踪算法研究

针对复杂环境下目标跟踪问题,提出了一种基于 有限差分扩展卡尔曼粒子滤波的多 特征自适应融合跟踪算法。采用有限差分扩展卡尔曼滤波器对采样粒子集合进行滤波更新, 通过融入最新观测信息的方法消弱权值退化现象;在新算法的框架内,利用目标静态和动态 互补特征作为观测量,实现不同环境下目标的多特征自适应融合跟踪。实验结果表明,本文 方法具有较好的跟踪精度和抗噪声干扰能力。     

一种改进的多特征融合目标跟踪算法

针对仅用单一颜色特征导致跟踪算法鲁棒性不高的问题,提出了一种改进的多特征融合目标跟踪算法。为了获得准确的目标颜色模型,提出了一种自适应选取目标颜色直方图的方法,同时利用LBP算子建立目标纹理特征模型,增强对目标的表征能力。在特征融合中引入特征不确定性度量方法,自适应调整不同特征对跟踪结果的贡献,有效地提高了传统粒子滤波算法的鲁棒性。实验结果表明,融合后的算法比传统的加性融合与乘性融合算法有更强的鲁棒性,能实现复杂场景下的目标跟踪,有效地描述目标。     

基于多个颜色分布模型的粒子滤波跟踪算法

基于粒子滤波的目标跟踪性能在很大程度上依赖于观测模型的选择.为了解决被跟踪目标外观特征变化导致模型漂移问题,提出了一种新的粒子滤波算法,利用目标外观的先验知识,为目标建立多个颜色模型,通过对目标函数的优化,采用最优凸组合模型实时地对目标进行跟踪.同时,采用UKF(Unscented Kalman Filter)产生粒子...     

一种基于均值移位的红外目标跟踪新方法

均值移位算法是一种搜索与样本点分布最相近模式的非参数统计方法．在彩色序列图像目标跟踪中，均值移位算法是一种有效的方法．但在红外目标跟踪中，由于单一灰度特征空间缺乏描述红外目标的信息，使得基于均值移位算法的红外目标跟踪不稳健．为了克服这个缺点，提出了构造级联灰度空间的红外目标跟踪新方案．同时，对于不同的红外图像序列使用不同的方法产生级联灰度空间．实验结果表明该方法对于红外小目标以及强杂波背景目标的跟踪是有效和稳健的．     

Object Modeling with Color Arrangement for Region‐Based Tracking

In this paper, we propose a new color histogram model for object tracking. The proposed model incorporates the color arrangement of the target that encodes the relative spatial distribution of the colors inside the object. Using the color arrangement, we can determine which color bin is more reliable for tracking. Based on the proposed color histogram model, we derive a mean shift framework using a modified Bhattacharyya distance. In addition, we present a method of updating an object scale and a target model to cope with changes in the target appearance. Unlike conventional mean shift based methods, our algorithm produces satisfactory results even when the object being tracked shares similar colors with the background.     

An Efficient Particle Filter–based Tracking Method Using Graphics Processing Unit (GPU)

Particle filter has been proven very robust in handling non-linear and non-Gaussian problems and has been widely used in the area of object tracking. One of the main problems in particle filter-based object tracking is, however, its high computational cost induced by the most time-consuming stage of measurement model computation. This paper makes progress in resolving the problem by proposing an efficient particle filter-based tracking algorithm using color information. First, a compact color cooccurrence histogram is presented, which considers both spatial and color information and can effectively represent color distribution with a very small number of histogram bins. The paper also introduces integral images by which the cooccurrence histogram can be obtained with simple array reference operations. However, the construction of the integral images on the CPU may be computationally expensive. Hence, this paper develops parallel algorithms on a desktop Graphics Processing Unit (GPU), which accomplishes the integral images construction and cooccurrence histogram computation after bin index determination. The resulting algorithm is quite efficient and has better performance than the traditional histogram-based tracking algorithm. The tracking time of the proposed algorithm increases insignificantly with the growth of particle number, and it remains consistent among varying image sequences and stable throughout all frames in the same image sequence due to its irrelevance to object size. Experiments in diverse image sequences validate our conclusions.     

Active contours for video object tracking using region, boundary and shape information

In this paper, we propose a robust model for tracking in video sequences with non-static backgrounds. The object boundaries are tracked on each frame of the sequence by minimizing an energy functional that combines region, boundary and shape information. The region information is formulated by minimizing the symmetric Kullback–Leibler (KL) distance between the local and global statistics of the objects versus the background. The boundary information is formulated using a color and texture edge map of the video frames. The shape information is calculated adaptively to the dynamic of the moving objects and permits tracking that is robust to background distractions and occlusions. Minimization of the energy functional is implemented using the level set method. We show the effectiveness of the approach for object tracking in color, infrared (IR), and fused color-infrared sequences.     

Spatially constrained level-set tracking and segmentation of non-rigid objects

Level-set is a widely used technique in segmentation-based tracking due to its flexibility in handling 2D topological changes and computational efficiency. Most existing level-set models aim at grouping pixels that have similar features into a region, without consideration of the spatial relationship of these pixels. In this paper, we present a novel level-set tracking method that incorporates spatial information to improve the robustness and accuracy of tracking non-rigid objects. Both tracking and segmentation are performed in a unified probabilistic framework, with additional spatial constraints from a part-based model—the Hough Forests. In the stage of tracking, the rigid motion of the target object is estimated by rigid registration in both the color space and the Hough voting space. Then in the stage of segmentation, some support points are obtained from back-projection, and guide the level-set evolution to capture the shape deformation. We conduct quantitative evaluation on two recently proposed public benchmarks: a non-rigid object tracking dataset and the CVPR2013 online tracking benchmark, involving 61 sequences in total. The experimental results demonstrate that our tracking method performs comparably to the state-of-the-arts in the CVPR2013 benchmark, while shows significantly improved performance in tracking non-rigid objects.     

复杂场景下基于多特征融合的视频跟踪

为了解决常见视频跟踪方法在复杂场景中难以有效跟踪运动物体的难题,研究了在粒子滤波框架下基于多特征融合的判别式视频跟踪算法.首先分析了特征提取和跟踪算法的鲁棒性和准确性的关系,指出融合多种特征能有效地提升算法在复杂场景中的跟踪效果,然后选择提取HSV颜色特征和HOG特征描述目标表观,并在线训练逻辑斯特回归分类器构造判别式目标表观模型.在公开的复杂场景视频进行测试,比较了使用单一特征和多种特征的实验效果,并且将所提算法和经典跟踪算法进行了比较,实验结果表明融合多种特征的视频跟踪更具鲁棒性和准确性.     

Stereo object tracking with fusion of texture,color and disparity information

A novel method for visual object tracking in stereo videos is proposed, which fuses an appearance based representation of the object based on Local Steering Kernel features and 2D color–disparity histogram information. The algorithm employs Kalman filtering for object position prediction and a sampling technique for selecting the candidate object regions of interest in the left and right channels. Disparity information is exploited, for matching corresponding regions in the left and right video frames. As tracking evolves, any significant changes in object appearance due to scale, rotation, or deformation are identified and embodied in the object model. The object appearance changes are identified simultaneously in the left and right channel video frames, ensuring correct 3D representation of the resulting bounding box in a 3D display monitor. The proposed framework performs stereo object tracking and it is suitable for application in 3D movies, 3D TV content and 3D video content captured by consuming stereo cameras. Experimental results proved the effectiveness of the proposed method in tracking objects under geometrical transformations, zooming and partial occlusion, as well as in tracking slowly deforming articulated 3D objects in stereo video.     

复杂背景下兼顾跟踪实时性和跟踪精度的目标跟踪技术研究

由于将CamShift算法在复杂背景和操作条件下应用于视频跟踪,跟踪失败和目标损失的现象将非常容易发生。为了提高复杂环境条件下目标跟踪的精度及实时性,本论文提出了一种能够在复杂环境条件下及时对目标对象进行追踪的技术。以颜色、纹理、目标动作信息的全面特性为基础对CamShift算法作出整改完善,通过组合Kalman过滤器预评估目标对象的动作情况,在目标对象受到制约的情况下,使用运转前的目标对象预先信息,对目标对象物体的动作轨迹执行最小平方运算以及外穿推进,同时基于对象物体的位移情况进行定位信息的预测评估,以助于恢复目标的定位信息直到制约情况结束。经多次实验,相关统计数据表明,这一算法能够用于复杂情形的环境条件下,且当目标对象处于短期闭塞情况下依然能达成目标的连续稳定追踪,在性能上具备出色的实时性。     

Robust Struck tracker via color Haar-like feature and selective updating

Recently, Struck—a tracker based on structured support vector machine, received great attention as a consequence of its superior performance on many challenging scenes. In this work, we present an improved Struck tracker by using color Haar-like features and effective selective updating. First, we integrate color information into Haar-like features in a simple way, which models the spatial and color information simultaneously without increasing the computational complexity. Second, we make selective model updates according to the tracking status of the object. This prevents inferior patterns resulted by occlusions, abrupt appearance or illumination changes from being added to object model, which decreases the risk of model drift problem. The experimental results indicate that the proposed tracking algorithm outperforms the original Struck by a remarkable margin in precision and accuracy, and it is competitive with other state-of-the-art trackers on a tracking benchmark of 50 challenging sequences.