一种运动增强混合概率假设密度滤波器用于视频监控场景下的实时多人跟踪

2013 IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (PETS) Pub Date : 2013-06-06 DOI:10.1109/PETS.2013.6523789

Volker Eiselein, T. Senst, I. Keller, T. Sikora

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引用次数: 5

摘要

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滤除大量杂波的能力而在跟踪界得到广泛应用。然而，它在计算机视觉场景中的应用可能很困难，因为它需要很高的检测概率。许多人类检测器都存在严重的不匹配率，这导致了PHD滤波器的问题。本文提出了一种利用光流信息增强高斯混合PHD (GM-PHD)滤波器的实现，以弥补漏检。我们对所提出的系统的参数进行了详细的数学讨论，并通过广泛的测试证明了我们的结果，这些测试显示了在几种环境和不同数据集上的性能。

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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A motion-enhanced hybrid Probability Hypothesis Density filter for real-time multi-human tracking in video surveillance scenarios

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.

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2013 IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (PETS)

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