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Comments on “Joint Detection and Estimation of Multiple Objects From Image Observations”

机译:关于“基于图像观察的多个物体的联合检测和估计”的评论

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摘要

The above article [1] introduced an algorithm for multitarget track-before-detect based on a multi-Bernoulli random finite set model (MB-TBD). This new algorithm was compared with the Histogram Probabilistic Multi-Hypothesis Tracker (H-PMHT) on simulated data examples containing multiple targets with non-linear dynamics. The authors reported poor performance from H-PMHT and described several deficiencies of the algorithm. This note highlights unnecessary assumptions made in the assessment of H-PMHT and repeats two of the simulation examples after relaxing them. We demonstrate a substantial improvement in performance compared with the originally published results. The simulation example is also shown to be a relatively high signal to noise problem and good performance is obtained from a conventional detect-then-track algorithm.
机译:上面的文章[1]介绍了一种基于多伯努利随机有限集模型(MB-TBD)的多目标检测前跟踪算法。在包含多个具有非线性动力学目标的模拟数据示例上,将此新算法与直方图概率多假设跟踪器(H-PMHT)进行了比较。作者报告说H-PMHT的性能较差,并描述了该算法的几个缺陷。本说明重点介绍了在评估H-PMHT时不必要的假设,并在放松了它们之后重复了两个模拟示例。与原始发布的结果相比,我们证明了性能的显着改善。该仿真示例还显示出是一个相对较高的信噪比问题,并且可以通过常规的“检测-跟踪-跟踪”算法获得良好的性能。

著录项

  • 来源
    《Signal Processing, IEEE Transactions on》 |2012年第3期|p.1539-1540|共2页
  • 作者

    Davey S. J.;

  • 作者单位

    Intelligence Surveillance and Reconnaissance Division, Defence Science and Technology Organisation, Edinburgh, Australia;

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  • 原文格式 PDF
  • 正文语种 eng
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