GM-PHD-Based Multi-Target Visual Tracking Using Entropy Distribution and Game Theory
GM-PHD-Based Multi-Target Visual Tracking Using Entropy Distribution and Game Theory
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DOI：
10.1109/tii.2013.2294156
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发表时间：
2014-05
影响因子：
12.3
通讯作者：
Xiaolong Zhou;Youfu Li;B. He;Tianxiang Bai
中科院分区：
文献类型：
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作者：
Xiaolong Zhou;Youfu Li;B. He;Tianxiang Bai
跟踪视频中的多个运动目标是一个挑战，因为有多种因素，包括视频数据噪声、目标数量变化以及相互遮挡问题。高斯混合概率假设密度(GM-PHD)滤波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。本文提出了一种将GM-PHD滤波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，实现了对噪声视频中新生儿目标的自动准确跟踪。在此基础上，提出了一种稳健的博弈论互遮挡处理算法，并结合改进的空间颜色外观模型对互遮挡目标进行了有效跟踪。通过引入遮挡区域内其他目标的干扰来改进空间颜色外观模型。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。
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.