Search - DATA ASSOCIATION AND TRACKING

Search - DATA ASSOCIATION AND TRACKING - List

[Software Engineering] r214 DL : 0: 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.
Date : 2025-12-23 Size : 126kb User : haiser
[Software Engineering] Online-Learning-for-Tracking DL : 0: 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!
Date : 2025-12-23 Size : 5.82mb User : 胡志恒
[Software Engineering] multisensor-data DL : 0: 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
Date : 2025-12-23 Size : 8.27mb User : Rakesh