Description: 多目标跟踪的Matlab仿真程序。用于数据处理和数据关联。-multi-target tracking Matlab simulation program. For the data processing and data association. Platform: |
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Author:房秉毅 |
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Description: Probabilistic Data Association Filter跟踪算法示例-Probabilistic Data Association Filter Tracking Algorithm example Platform: |
Size: 3072 |
Author:liushan |
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Description: 多模型和概率数据关联结合后的IMMPDA算法,主要用于雷达数据处理,单目标的在杂波环境下的目标跟踪。-Multi-model and probabilistic data association after combining IMMPDA algorithm, mainly used for radar data processing, a single goal in the cluttered environment of the target tracking. Platform: |
Size: 4096 |
Author:lgvee |
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Description: 性能优化的跟踪门算法
一个基于数据关联性能评价的优化跟踪门算法,并通过它来减少跟踪门内来自非本目标的回
波,最终达到提高多目标多传感器跟踪系统性能的目的)与最优跟踪门相比,经理论分析和仿真数据表明,本算法有效
改善了系统的性能,尤其在强干扰、高虚警的情况下更为明显)-Performance Optimization of Tracking Gate Algorithm of a performance evaluation based on the data association Tracking Gate Algorithm optimization, and through it to reduce the tracking of the target sector from non-echo, and ultimately to improve multi-target multi-sensor tracking system for the purpose of performance) and compared to optimal tracking of the door by the theoretical analysis and simulation data show that the algorithm effectively improve the system performance, especially in strong interference, and high false alarm more obvious cases) Platform: |
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Author:88txj |
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Description: A new approach toward target representation and localization, the central component in visual tracking
of non-rigid objects, is proposed. The feature histogram based target representations are regularized
by spatial masking with an isotropic kernel. The masking induces spatially-smooth similarity functions
suitable for gradient-based optimization, hence, the target localization problem can be formulated using
the basin of attraction of the local maxima. We employ a metric derived from the Bhattacharyya
coefficient as similarity measure, and use the mean shift procedure to perform the optimization. In the
presented tracking examples the new method successfully coped with camera motion, partial occlusions,
clutter, and target scale variations. Integration with motion filters and data association techniques is also
discussed. We describe only few of the potential applications: exploitation of background information,
Kalman tracking using motion models, and face tracking.-A new approach toward target representation and localization, the central component in visual trackingof non-rigid objects, is proposed. The feature histogram based target representations are regularizedby spatial masking with an isotropic kernel. The masking induces spatially-smooth similarity functionssuitable for gradient-based optimization, hence, the target localization problem can be formulated usingthe basin of attraction of the local maxima. We employ a metric derived from the Bhattacharyyacoefficient as similarity measure, and use the mean shift procedure to perform the optimization. In thepresented tracking examples the new method successfully coped with camera motion, partial occlusions, clutter, and target scale variations. Integration with motion filters and data association techniques is alsodiscussed. We describe only few of the potential applications: exploitation of background information, Kalman tracking using motion models, and face tracking . Platform: |
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Author: |
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Description: 一种用于多目标跟踪的改进PDA算法,北京理工大学学报上面的文章,、概率数据
关联滤波(p robab ility data associat ion f ilter, PDA )-A multi-target tracking algorithm improvements PDA, Beijing Institute of Technology Journal of the above articles, probabilistic data association filter (p robab ility data associat ion f ilter, PDA) Platform: |
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Author:孟钢 |
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Description: 提出一种新的目标表示和定位方法,该方法是非刚体跟踪的核心技术.利用均质空间掩膜规范基于特征直方图的目标表示,该掩膜引入了适合于梯度优化的空间平滑相似函数,所以可以将目标定位问题转换为局部极大值求解问题.我们利用从Bhattacharyya系数倒出的规则作为相似度量,利用mean shift procedure完成优化求解.在给出的测试用例中, 本文方法成功解决了相机移动,阴影,以及其他的图象噪声干扰.文章对运动滤波和数据关联技术的集成也进行了讨论.-A new objective and positioning method to track non-rigid body' s core technology. Standardizing the use of homogeneous space mask the characteristics of histogram based on the objectives that the mask is suitable for the introduction of gradient optimization is similar to spatial smoothing function, Therefore, targeting the problem can be converted to solve the problem of local maxima. We poured from the rules of Bhattacharyya coefficient as similarity measure, using mean shift procedure for solving optimization. give the test cases in, the method succeeded in solving the camera Mobile, shadows, and other image noise. article on the campaign filtering and data association techniques of integration were also discussed. Platform: |
Size: 2700288 |
Author:maolei |
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Description: A new approach toward target representation and localization, the central component in visual tracking of nonrigid objects,
is proposed. The feature histogram-based target representations are regularized by spatial masking with an isotropic kernel. The
masking induces spatially-smooth similarity functions suitable for gradient-based optimization, hence, the target localization problem
can be formulated using the basin of attraction of the local maxima. We employ a metric derived from the Bhattacharyya coefficient as
similarity measure, and use the mean shift procedure to perform the optimization. In the presented tracking examples, the new method
successfully coped with camera motion, partial occlusions, clutter, and target scale variations. Integration with motion filters and data
association techniques is also discussed. We describe only a few of the potential applications: exploitation of background information,
Kalman tracking using motion models, and face tracking. Platform: |
Size: 2459648 |
Author:Ali |
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Description: An Interacting Multipattern Probabilistic Data
Association (IMP-PDA) Algorithm for Target
Tracking Platform: |
Size: 289792 |
Author:ali/ahmadiyaan |
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Description: A multi-target tracking toolbox based on the MTT Library of the InstantVision ISE with expanded functionality and tools for off-line design, analysis and testing. The toolbox contains the implementation of distance calculation methods (e.g. city-block based), data assignment and association strategies (e.g. ENN and JVC), state prediction filters (e.g. IMM) with video marking and debugging tools in order to support a complex multi-target tracking system design. Platform: |
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Author:Aka |
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Description: PMHT是一个优秀的跟踪算法,具有灵活性和易修正的特点。-The probabilistic multihypothesis tracker (PMHT) is a
target tracking algorithm of considerable theoretical elegance.
In practice, its performance turns out to be at best similar
to that of the probabilistic data association filter (PDAF)
and since the implementation of the PDAF is less intense
numerically the PMHT has been having a hard time finding
acceptance. The PMHT’s problems of nonadaptivity, narcissism,
and over-hospitality to clutter are elicited in this work. The
PMHT’s main selling-point is its flexible and easily modifiable
model, which we use to develop the “homothetic” PMHT
maneuver-based PMHTs, including those with separate and
joint homothetic measurement models a modified PMHT whose
measurement/target association model is more similar to that
of the PDAF and PMHTs with eccentric and/or estimated
measurement models. Platform: |
Size: 439296 |
Author:wang zhuo |
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Description: 多假设跟踪算法(MHT)是一种在数据关联发生冲突时,形成多种假设以延迟做决定的逻辑。与PDA合并多种假设的做法不同,MHT算法把多个假设继续传递,让后续的观测数据解决这种不确定性。举个例子,PDA对所有假设以对应的概率进行加权平均,然后再对航迹进行更新。因此,如果有10个假设,PDA会将这10个假设有效的合并只留下一个假设。而另一方面,MHT却是保持这10个假设的子集并延迟决定,这样可以利用之后的观测数据解决当前扫描帧的不确定性问题。 -Multiple Hypothesis Tracking (MHT) is a kind of data association in the event of a conflict, the formation of a variety of assumptions in order to delay a decision logic. PDA combined with the practice of a variety of different assumptions, MHT algorithm is to pass on to a number of assumptions, so that follow-up observations to resolve this uncertainty. For example, PDA for all assumptions to the corresponding probability-weighted average, and then update the right track. Therefore, if there are 10 assumptions, PDA will be assumed that an effective merger of 10, leaving only a hypothesis. On the other hand, MHT was to keep this a subset of 10 hypothetical and delay the decision, so that after the observational data can be used to resolve the current scan frame uncertainties. Platform: |
Size: 129024 |
Author:haiser |
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Description: (交互式多模型算法)目标跟踪程序,java语言编写,包含了kalman滤波。这种方法的特点是在各模型之间“转换”,自动调节滤波带宽,和适合机动目标的跟踪。可以直接调用,附有示例代码-A multi-target tracking toolbox based on the MTT Library of the InstantVision ISE with expanded functionality and tools for off-line design, analysis and testing. The toolbox contains the implementation of distance calculation methods (e.g. city-block based), data assignment and association strategies (e.g. ENN and JVC), state prediction filters (e.g. IMM) with video marking and debugging tools in order to support a complex multi-target tracking system design. Platform: |
Size: 14336 |
Author:june |
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Description: 数据关联是多目标跟踪的一项关键技术。JPDA是大家公认的多目标跟踪中性能较好的数据关联算法,它
认为量测和目标是一一对应的关联关系,但在许多实际情况中,量测和目标是多一多对应的关系。针对上述情况,该文提
出了广义概率数据关联算法(Generalized Probability Data Association,GPDA)。文中从理论上对这两种算法的性能进行了
详细分析,并利用Monte Carlo技术对其性能进行了仿真比较。-Data association is one of the key technologies in multi—target tracking.And JPDA is considered as the best da·
ta association method.JPDA considers the association of measurements with targets is simply one-to-one problem.But in many
practical cases,the association of measurements with targets will be multiple—to—multiple problem.For this case,a
Generalized
Probability Data Association(GPDA)algorithm is proposed in this paper.Furthermore,this paper analyzes the performance of
these two algorithms theoretically.And we give the comparative analysis of those performances by using Monte Carlo method. Platform: |
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Author:minnie |
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Description: 用最邻近数据关联算法实现目标跟踪,对研究目标跟踪领域的朋友有用-With the nearest neighbor data association algorithm for target tracking Platform: |
Size: 20480 |
Author:郝人 |
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Description: 本书为卡耐基梅隆大学教授Robert T. Collins在中美学术交流会上专门为中国学生做的关于目标跟踪方面的讲座,内容涵盖了template matching, mean-shift, data association等。同时结合了他们实验室的项目经验,讲解内容深入浅出,全力推荐!-Book for the Carnegie Mellon University Robert T. Collins in the United States specifically for academic exchange at the Chinese students do talk about tracking aspects, covering the template matching, mean-shift, data association and so on. Combined with their experience of the laboratory project, explain the content easy to understand, fully recommended! Platform: |
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Author:胡志恒 |
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Description: 主要内容有:雷达数据处理概述(包括研究目的、意义、历史和现状等),参数估计与线性滤波方法,非线性滤波方法,量测数据预处理技术,多目标跟踪中的航迹起始,极大似然类多目标数据互联方法,贝叶斯类多目标数据互联方法,机动目标跟踪,群目标跟踪,多目标跟踪终结理论与航迹管理,无源雷达数据处理,脉冲多普勒和相控阵雷达数据处理,雷达组网数据处理,雷达数据处理性能评估,雷达数据处理仿真技术,雷达数据处理的实际应用,以及关于雷达数据处理理论的回顾、建议与展望。-The main contents are: an overview of the radar data processing (including research purpose, meaning, history and current status, etc.), parameter estimation method with linear filtering, nonlinear filtering method, measurement data preprocessing techniques, starting track multiple target tracking, maximum Likelihood class of multi-target data interconnection methods, Bayesian methods class multi-target data association, target tracking, target tracking group, multi-target tracking end theory and track management, passive radar data processing, pulsed Doppler and phased array radar data processing, radar network data processing, radar data processing performance evaluation, radar data processing simulation technology, the practical application of radar data processing, as well as a review on radar data processing theory, suggestions and prospects. Platform: |
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Author:dabin |
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Description: 提出了一种新的概率数据互联和粒子滤波相结合的新算法,并应用于杂波环境下的无源声纳系统中,该算法也可以很容易的应用于多目标情形。-There proposed a new method of data association called highest probability data association (HPDA) combined with
particle filtering and applied to passive sonar tracking in clutter.The proposed HPDA algorithm can
be easily extended to multi-target tracking problems. Platform: |
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Author:ST |
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