弥合第一人称视频中原子和复杂活动之间的差距

Bradley Schneider, Tanvi Banerjee
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引用次数: 0

摘要

在这项工作中,我们描述了一个使用模糊推理系统对第一人称视频中的活动进行分类的系统。我们的模糊推理系统建立在传统的基于对象和运动的视频特征之上,并根据多个模糊输出变量提供活动描述。我们演示了模糊系统在一个众所周知的无脚本第一人称视频数据集上的应用,将动作分为四类。将结果与其他监督学习技术和最先进的技术进行比较,我们发现我们的模糊系统优于其他选择。此外,模糊输出具有比传统分类器更具描述性的潜力,因为它们能够处理不确定性并产生可解释的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Bridging the Gap between Atomic and Complex Activities in First Person Video
In this work, we describe a system for classifying activities in first-person video using a fuzzy inference system. Our fuzzy inference system is built on top of traditional object-and motion-based video features and provides a description of activities in terms of multiple fuzzy output variables. We demonstrate the application of the fuzzy system on a well known dataset of unscripted first person videos to classify actions into four categories. Comparing the results to other supervised learning techniques and the state-of-the-art, we find that our fuzzy system outperforms alternatives. Further, the fuzzy outputs have the potential to be much more descriptive than conventional classifiers due to their ability to handle uncertainty and produce explainable results.
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