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首页> 外文会议>2013 IEEE International Workshop on Performance Evaluation of Tracking and Surveillance >A motion-enhanced hybrid Probability Hypothesis Density filter for real-time multi-human tracking in video surveillance scenarios
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A motion-enhanced hybrid Probability Hypothesis Density filter for real-time multi-human tracking in video surveillance scenarios

机译：运动增强型混合概率假设密度过滤器，用于视频监视场景中的实时多人跟踪

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摘要

The Probability Hypothesis Density (PHD) filter is a multi-object Bayes filter which has been recently becoming popular in the tracking community especially for its linear complexity and its ability to filter out a high amount of clutter. However, its application to Computer Vision scenarios can be difficult as it requires high detection probabilities. Many human detectors suffer from a significant miss-match rate which causes problems for the PHD filter. This article presents an implementation of a Gaussian Mixture PHD (GM-PHD) filter which is enhanced by Optical Flow information in order to account for missed detections. We give a detailed mathematical discussion for the parameters of the proposed system and justify our results by extensive tests showing the performance in several contexts and on different datasets.
机译：概率假设密度（PHD）过滤器是一种多对象贝叶斯过滤器，最近因其线性复杂性和滤除大量杂波的能力而在跟踪社区中变得越来越流行。但是，由于它需要很高的检测概率，因此很难将其应用于计算机视觉场景。许多人类探测器的失配率很高，这会导致PHD滤波器出现问题。本文介绍了一种高斯混合PHD（GM-PHD）滤波器的实现，该滤波器通过光流信息进行了增强，以解决错过的检测问题。我们对拟议系统的参数进行了详细的数学讨论，并通过广泛的测试来证明我们的结果合理，这些测试显示了在多种情况下以及在不同数据集上的性能。

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