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Hierarchical Charged Particle Filter for Multiple Target Tracking.

机译：用于多目标跟踪的分层带电粒子滤波器。

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摘要

Multiple Object Tracking is a significant application of vision-based autonomous solutions. Although it is a logical extension of single object tracking, those algorithms do not simply scale over to the multiple object case. Tracking multiple objects brings on its own set of challenges and complexity.;A multiple object tracking system requires a set of tasks to be completed: (a) Objects need to be detected using Detection algorithms; (b) Objects need to be tracked using Tracking algorithms under conditions of object movement, occlusion, scale changes, illumination changes, scene movement etc.; (c) Such a system must perform as efficiently as possible, with the goal of real time execution. Combining these tasks presents its own set of challenges. This thesis provides a vision based system to track multiple objects under such variety of constraints.;A typical vision-based detection algorithm uses non-uniform convolution as one of its operations. This thesis provides a unique method to perform very fast convolution. The method called Stacked Integral Image builds upon the concept of box filters and integral image to accelerate convolution performed in vision based systems.;As tracking algorithms must handle changing scale/size of objects while tracking them, objects can become so small/blurred that geometry based detectors which use corners/edges etc. become unreliable. To utilize radiometric features for object detection, this thesis provides a unique method called Pattern Recognition by Cluster Accumulation that uses clustering and Hough style accumulation to reliably detect objects when they are small/blurred.;The particle filter is a powerful tool to track an object undergoing non-linear state dynamics with non-Gaussian noise. When tracking multiple objects using multiple particle filters, the particles of non-dominant targets tend to get hijacked by dominant targets. This problem is solved by a new resampling method called Charged Resampling which uses an electric charge like potential in a probabilistic setting to minimize particle hijack.;To handle moving objects in a moving scene, the problem of double dynamics is solved by employing a layered method called Hierarchical Particle Filter. This method cleanly separates scene tracking from multiple object tracking with a feedback connection to transfer intelligence from one sub-system to another.;Hence the novel contributions of this thesis are: (a) Fast convolution method, (b) Radio-metric detection algorithm, (c) Charged particle filter resampling, and (d) Hierarchical particle filter setup.
机译：多对象跟踪是基于视觉的自主解决方案的重要应用。尽管这是单对象跟踪的逻辑扩展，但是这些算法并不能简单地扩展到多对象情况。跟踪多个对象带来了自己的挑战和复杂性。多对象跟踪系统需要完成一组任务：（a）需要使用检测算法来检测对象； （b）需要在物体移动，遮挡，尺度变化，照度变化，场景移动等情况下使用跟踪算法对物体进行跟踪； （c）这种系统必须以实时执行为目标，尽可能高效地执行。将这些任务组合在一起会带来一系列挑战。本文提供了一种基于视觉的系统，可以在多种约束条件下跟踪多个物体。一种典型的基于视觉的检测算法，将非均匀卷积作为其操作之一。本文提供了一种独特的方法来执行非常快速的卷积。称为堆叠积分图像的方法建立在框式滤镜和积分图像的概念上，以加速在基于视觉的系统中执行的卷积;由于跟踪算法必须在跟踪对象时改变其缩放比例/大小，因此对象可能变得如此小/模糊，以至于几何形状使用拐角/边缘等的基于探测器的探测器变得不可靠。为了利用辐射特征进行物体检测，本文提供了一种独特的方法，即通过聚类和霍夫样式聚类技术，通过聚类累积识别模式，可以在小/模糊物体时可靠地检测它们。粒子过滤器是跟踪物体的强大工具。经历具有非高斯噪声的非线性状态动力学。当使用多个粒子过滤器跟踪多个对象时，非主要目标的粒子往往会被主要目标劫持。这个问题是通过一种称为电荷重采样的新重采样方法解决的，该方法在概率设置中使用像电势这样的电荷来最大程度地减少粒子劫持。为了处理运动场景中的运动物体，采用分层方法解决了双重动力学问题。称为层次粒子过滤器。这种方法干净利落地将场景跟踪与具有反馈连接的多对象跟踪分开，以将情报从一个子系统转移到另一个子系统。因此，本论文的新颖贡献是：（a）快速卷积方法，（b）辐射检测算法，（c）带电粒子滤波器重采样和（d）层次粒子滤波器设置。

著录项

  • 作者

    Bhatia, Amit.;

  • 作者单位

    North Carolina State University.;

  • 授予单位 North Carolina State University.;
  • 学科 Computer Science.
  • 学位 Ph.D.
  • 年度 2011
  • 页码 227 p.
  • 总页数 227
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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