Pairwise Threshold for Gaussian Mixture Classification and Its Application on Human Tracking Enhancement

Daegeon Kim, S. Lee
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引用次数: 2

Abstract

In this paper, we describe Object Pixel Mixture Classifiers (OPMCs) which classify an object not only apart from background but also from other objects based on Gaussian Mixture Model (GMM) classification. The proposed OPMC is different from general GMM based classifiers in the respect that novel pairwise threshold is applied for final classification. Pairwise thresholds are different thresholds depending on predicted mixture component index combination by a positive and a negative GMMs. We train the pairwise threshold using discriminative model so that generative GMM can take advantage from it. We demonstrate that OPMCs are robust to noise in train data and can keep tracking objects after missing tracks even with occlusion. Also, we show that OPMCs can generate meaningful blob of object, and can separate the region of objects from merged blobs.
高斯混合分类的两两阈值及其在人体跟踪增强中的应用
在本文中,我们描述了基于高斯混合模型(GMM)分类的目标像素混合分类器(OPMCs),该分类器不仅可以将目标与背景分开,还可以将目标与其他目标分开。与一般基于GMM的分类器不同,本文提出的OPMC在最终分类中采用了新的两两阈值。两两阈值是不同的阈值取决于预测的混合成分指数组合的正负GMMs。我们使用判别模型训练两两阈值,使生成式GMM能够利用它。我们证明了opmc对列车数据中的噪声具有鲁棒性,并且即使有遮挡也可以在缺失轨道后保持跟踪目标。此外,我们还证明了opmc可以生成有意义的目标blob,并可以将目标区域从合并的blob中分离出来。
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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