Unsupervised learning of a scene-specific coarse gaze estimator

Ben Benfold, I. Reid
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引用次数: 69

Abstract

We present a method to estimate the coarse gaze directions of people from surveillance data. Unlike previous work we aim to do this without recourse to a large hand-labelled corpus of training data. In contrast we propose a method for learning a classifier without any hand labelled data using only the output from an automatic tracking system. A Conditional Random Field is used to model the interactions between the head motion, walking direction, and appearance to recover the gaze directions and simultaneously train randomised decision tree classifiers. Experiments demonstrate performance exceeding that of conventionally trained classifiers on two large surveillance datasets.
场景特定的粗凝视估计器的无监督学习
提出了一种从监控数据中估计人的粗注视方向的方法。与以前的工作不同,我们的目标是在不依赖于大量手工标记的训练数据语料库的情况下做到这一点。相反,我们提出了一种学习分类器的方法,没有任何手工标记的数据,只使用自动跟踪系统的输出。使用条件随机场对头部运动、行走方向和外观之间的相互作用进行建模,以恢复凝视方向,同时训练随机决策树分类器。实验表明,在两个大型监控数据集上,分类器的性能优于常规训练的分类器。
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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