Real-Time Multi-human Tracking Using a Probability Hypothesis Density Filter and Multiple Detectors

Volker Eiselein, Dan Arp, Michael Pätzold, T. Sikora
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引用次数: 72

Abstract

The Probability Hypothesis Density (PHD) filter is a multi-object Bayes filter which has recently attracted a lot of interest in the tracking community mainly for its linear complexity and its ability to deal with high clutter especially in radar/sonar scenarios. In the computer vision community however, underlying constraints are different from radar scenarios and have to be taken into account when using the PHD filter. In this article, we propose a new tree-based path extraction algorithm for a Gaussian Mixture PHD filter in Computer Vision applications. We also investigate how an additional benefit can be achieved by using a second human detector and justify an approximation for multiple sensors in low-clutter scenarios.
基于概率假设密度滤波和多检测器的实时多人跟踪
概率假设密度(PHD)滤波器是一种多目标贝叶斯滤波器,近年来由于其线性复杂性和处理高杂波(特别是雷达/声纳场景)的能力而引起了跟踪界的广泛关注。然而,在计算机视觉社区中,潜在的约束与雷达场景不同,在使用PHD滤波器时必须考虑到这些约束。在本文中,我们提出了一种新的基于树的高斯混合PHD滤波器路径提取算法。我们还研究了如何通过使用第二个人工探测器来获得额外的好处,并证明了在低杂波情况下多个传感器的近似。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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