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首页> 外文期刊>IEEE transactions on industrial informatics >Walsh–Hadamard-Kernel-Based Features in Particle Filter Framework for Underwater Object Tracking
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Walsh–Hadamard-Kernel-Based Features in Particle Filter Framework for Underwater Object Tracking

机译：基于WALSH-HADAMARD-KERNEL的粒子过滤器框架的特征，用于水下对象跟踪

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One of the well-established research domains among computer vision scientists is object tracking. However, not much work has been done in underwater scenarios. This article addresses the problem of visual tracking in the underwater environment with the stationary and nonstationary camera setups. In order to deal with the underwater optical dynamics, a dominant color component-based scene representation is employed in the YCbCr color space. An adaptive approach is devised to select the Walsh-Hadamard (WH) kernels for the efficient extraction of color, edge, and texture strengths, whereas a new feature called range strength is proposed to extract the variation of intensity from underwater sequences in the local neighborhood using the WH kernel. The likelihood of these feature strengths is integrated in a particle filter framework to track the object of interest in underwater sequences. The reference feature strengths used in assigning weights to the particles are updated based on the Sorensen distance. The coefficients of feature strengths are calculated in such a way that if one feature fails, then its coefficient become insignificant, whereas the more suitable features get higher feature coefficients. The effectiveness of the proposed scheme is evaluated using the underwater video datasets: reefVid, fish4knowledge (F4K), underwaterchangedetection (UWCD), and National Oceanic and Atmospheric Administration (NOAA). The performance evaluation is performed by comparing the scheme with five recent state-of-the-art tracking schemes. The quantitative analysis of the proposed scheme is carried out using three evaluation measures: overall intersection over union, centroid location error, and average tracking error. The performance of the proposed scheme is quite encouraging in the case of sequences with hazy and degraded, partially occluded, and camouflaged challenges.
机译：计算机视觉科学家之间的既熟悉的研究领域之一是对象跟踪。然而，在水下情景中没有做出太多的工作。本文通过静止和非间断相机设置解决了水下环境中的视觉跟踪问题。为了处理水下光学动力学，在YCBCR颜色空间中采用基于主基的基于组件的场景表示。设计了一种自适应方法，以选择沃尔什 - Hadamard（WH）内核，以便有效地提取颜色，边缘和纹理强度，而提出了一种称为范围强度的新特征，以提取当地邻域中的水下序列的强度变化使用wh内核。这些特征强度的可能性集成在粒子过滤器框架中，以跟踪水下序列的感兴趣对象。基于Sorensen距离更新用于分配给粒子的权重的参考特征强度。特征强度的系数以这样的方式计算，即如果一个特征发生故障，则其系数变得微不足道，而更合适的特征可以获得更高的特征系数。使用水下视频数据集进行评估所提出的方案的有效性：Reefvid，Fish4knowledge（F4K），疏松沉积（UWCD）和国家海洋和大气管理（NOAA）。通过将该方案与五个最新的最先进的跟踪方案进行比较来执行性能评估。采用三种评估措施进行拟议方案的定量分析：联盟，质心位置误差和平均跟踪误差的整体交叉口。在具有朦胧和降级，部分闭塞和伪装挑战的情况下，拟议方案的表现非常令人鼓舞。

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