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首页> 外文期刊>IEEE transactions on industrial informatics >GM-PHD-Based Multi-Target Visual Tracking Using Entropy Distribution and Game Theory
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GM-PHD-Based Multi-Target Visual Tracking Using Entropy Distribution and Game Theory

机译：基于GM-PHD的熵分配和博弈论的多目标视觉跟踪

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摘要

Tracking multiple moving targets in a video is a challenge because of several factors, including noisy video data, varying number of targets, and mutual occlusion problems. The Gaussian mixture probability hypothesis density (GM-PHD) filter, which aims to recursively propagate the intensity associated with the multi-target posterior density, can overcome the difficulty caused by the data association. This paper develops a multi-target visual tracking system that combines the GM-PHD filter with object detection. First, a new birth intensity estimation algorithm based on entropy distribution and coverage rate is proposed to automatically and accurately track the newborn targets in a noisy video. Then, a robust game-theoretical mutual occlusion handling algorithm with an improved spatial color appearance model is proposed to effectively track the targets in mutual occlusion. The spatial color appearance model is improved by incorporating interferences of other targets within the occlusion region. Finally, the experiments conducted on publicly available videos demonstrate the good performance of the proposed visual tracking system.
机译：跟踪视频中的多个移动目标是一项挑战，因为有多个因素，包括嘈杂的视频数据，目标数量不定以及相互遮挡问题。高斯混合概率假设密度（GM-PHD）过滤器旨在递归传播与多目标后验密度相关的强度，可以克服数据关联带来的困难。本文开发了一种将GM-PHD过滤器与目标检测相结合的多目标视觉跟踪系统。首先，提出了一种新的基于熵分布和覆盖率的出生强度估计算法，以自动，准确地跟踪嘈杂视频中的新生儿目标。然后，提出了一种鲁棒的博弈论互遮挡处理算法，该算法具有改进的空间颜色外观模型，可以有效地跟踪互遮挡中的目标。通过将其他目标的干扰纳入遮挡区域，可以改善空间颜色外观模型。最后，在公开视频上进行的实验证明了所提出的视觉跟踪系统的良好性能。

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