Human Action Recognition in Large-Scale Datasets Using Histogram of Spatiotemporal Gradients

K. Reddy, Naresh P. Cuntoor, A. Perera, A. Hoogs
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引用次数: 31

Abstract

Research in human action recognition has advanced along multiple fronts in recent years to address various types of actions including simple, isolated actions in staged data (e.g., KTH dataset), complex actions (e.g., Hollywood dataset) and naturally occurring actions in surveillance videos (e.g, VIRAT dataset). Several techniques including those based on gradient, flow and interest-points have been developed for their recognition. Most perform very well in standard action recognition datasets, but fail to produce similar results in more complex, large-scale datasets. Here we analyze the reasons for this less than successful generalization by considering a state-of-the-art technique, histogram of oriented gradients in spatiotemporal volumes as an example. This analysis may prove useful in developing robust and effective techniques for action recognition.
基于时空梯度直方图的大规模数据集人体动作识别
近年来,人类动作识别的研究在多个方面取得了进展,以解决各种类型的动作,包括阶段数据(例如,KTH数据集)中的简单,孤立动作,复杂动作(例如,好莱坞数据集)和监控视频中自然发生的动作(例如,VIRAT数据集)。基于梯度法、流量法和兴趣点法的识别技术得到了发展。大多数在标准动作识别数据集中表现很好,但在更复杂的大规模数据集中却无法产生类似的结果。在这里,我们通过考虑最先进的技术来分析这种不太成功的概括的原因,以时空体中定向梯度直方图为例。这一分析可能有助于开发稳健有效的动作识别技术。
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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