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: |
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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: 数据关联算法在目标跟踪中的应用，用matlab语言实现的。-Data association algorithm for target tracking in the application, using matlab language achievable. Platform: |
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Author:文如泉 |
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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: 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: |
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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: 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: |
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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: |
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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: |
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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: 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: |
Size: 6106112 |
Author:胡志恒 |
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Description: Part I Introduction to Multisensor Data Fusion
1
Multisensor Data Fusion
David L. Hall and James Llinas
1.1 Introduction
1.2 Multisensor Advantages
1.3 Military Applications
1.4 Nonmilitary Applications
1.5 Three Processing Architectures
1.6 A Data Fusion Process Model
1.7 Assessment of the State of the Art
1.8 Additional Information
Reference
2
Revisions to the JDL Data Fusion Model
Alan N. Steinberg and
Christopher L. Bowman
2.1 Introduction
2.2 What Is Data Fusion? What Isn’t?
2.3 Models and Architectures
2.4 Beyond the Physical
2.5 Comparison with Other Models
2.6 Summary
References
3
Introduction to the Algorithmics of Data Association in Multiple-Target Tracking
Jeffrey K. Uhlmann
3.1 Introduction
3.2 Ternary Trees
3.3 Priority kd-Trees
3.4 Conclusion
Acknowledgments
References Platform: |
Size: 8673280 |
Author:Rakesh |
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Description: 多模型与概率数据关联结合后的IMMPDA算法，主要要用于雷达数据处理，单目标的在杂波环境下的目标跟踪。
-Multiple model probabilistic data association IMMPDA algorithm to be used for radar data processing, target tracking of a single target in clutter environment. Platform: |
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Author:lnwjyy |
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Description: An effective approach to the detection and tracking of
small moving targets with low contrast is proposed-The detection and tracking of small moving targets in low
signal-to-noise ratio and cluttered environments is a very
important problem in surveillance and target tracking [l].
In the past two decades, extensive research has been
carried out to solve the problem, including Kalman filter,
probabilistic data association, multiple hypothesis testing
121 and etc. Platform: |
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Author:蒋星星 |
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Description: 多目标跟踪中的交互式多模型数据关联，仅供参考，-Interactive multi-model data association in multi-target tracking for reference only, thank you Platform: |
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Author:吕鑫 |
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Description: 在运动的位置叠加噪声。进行JPDA概率数据关联及kalman滤波。
两运动目标在x-y平面做匀速直线运动。初始位置是（4000,1200）（300,1500）速度分别是（200,200）（400,200）传感器对量目标进行位置状态量测。
采样间隔T=1,点数n=80.检测概率为1，正确量测落入跟踪内的概率为0.99，杂波均匀分布的密度为2个/km2由RAND函数产生在[0，1]上均匀分布的随机变量，跟踪门限为9.21。
-Superimposed noise in the position of the movement. JPDA probabilistic data association and kalman filtering. Two moving targets uniform linear motion in the xy plane. The initial position (4000,1200) (300,1500) speed (200,200) (400,200) position sensor on the amount of target state measurements. Sampling interval T = 1, points n = 80. Detection probability of correctly measured fall into the tracking probability 0.99, 2/km2 clutter uniform distribution of density generated by the RAND function [0,1] uniformly distributed random variables tracking threshold of 9.21. Platform: |
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Author:雪 |
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Description: 在matlab环境下，用最邻近数据关联算法实现目标跟踪。--In the matlab environment, with the nearest neighbor data association target tracking algorithm.- Platform: |
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Author:潘朝 |
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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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Description: Over-the-horizon radar (OTHR) exploits skywave propagation
of high-frequency signals to detect and track targets,
which are different from the conventional radar. It
has received wide attention because of its wide area surveillance,
long detection range, strong anti-stealth ability,
the capability of the long early warning time, and so on.
In OTHR, a significant problem is the effect of multipath
propagation, which causes multiple detections via
different propagation paths for a target with missed detections
and false alarms at the receiver [1–6]. Nevertheless,
the conventional tracking algorithms, such as
probabilistic data association (PDA) [7–9], presume that
a single-measurement per target, it may consider the
other measurements of the same target as clutter, and
multiple tracks are produced when a single target is
present. Therefore, these methods cannot effectively
solve the multipath propagation problem.(Conventional multitarget tracking systems presume that each target can produce at most one measurement
per scan. Due to the multiple ionospheric propagation paths in over-the-horizon radar (OTHR), this assumption is
not valid. To solve this problem, this paper proposes a novel tracking algorithm based on the theory of finite set
statistics (FISST) called the multipath probability hypothesis density (MP-PHD) filter in cluttered environments.
First, the FISST is used to derive the update equation, and then Gaussian mixture (GM) is introduced to derive
the closed-form solution of the MP-PHD filter. Moreover, the extended Kalman filter (EKF) is presented to deal
with the nonlinear problem of the measurement model in OTHR. Eventually, the simulation results are provided
to demonstrate the effectiveness of the proposed filter.) Platform: |
Size: 18432 |
Author:ioeyoyo |
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Search - data association in tracking