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首页> 外文期刊>Journal of visual communication & image representation >Human action recognition employing negative space features
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Human action recognition employing negative space features

机译：利用负空间特征的人体动作识别

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摘要

We proposed a region based method to recognize human actions from video sequences. Unlike other region based methods, it works with the surrounding regions of the human silhouette termed as negative space. This paper further extends the idea of negative space to cope with the changes in viewpoints. It also addresses the problem of long shadows which is one of the major challenges of human action recognition. Some systems attempt suppressing shadows during the segmentation process but our system takes input of segmented binary images of which the shadow is not suppressed. This makes our system less dependent on segmentation process. Further, this approach can complement the positive space (silhouette) based methods to boost recognition. The system consists of a hierarchical processing: histogram analysis on segmented input image, followed by motion and shape feature extraction, pose sequence analysis by employing Dynamic Time Warping and at last classification by Nearest Neighbor classifier. We evaluated our system by most commonly used datasets and achieved higher accuracy than the state of the arts methods. Our system can also retrieve video sequences from queries of human action sequences.
机译：我们提出了一种基于区域的方法来识别视频序列中的人类动作。与其他基于区域的方法不同，它与人类轮廓被称为负空间的周围区域一起使用。本文进一步扩展了负空间的概念，以应对观点的变化。它还解决了长阴影问题，这是人类行为识别的主要挑战之一。一些系统尝试在分割过程中抑制阴影，但是我们的系统采用不抑制阴影的分割二进制图像的输入。这使我们的系统减少了对细分过程的依赖。此外，该方法可以补充基于正空间（轮廓）的方法以增强识别能力。该系统由以下分层处理组成：对分割的输入图像进行直方图分析，然后进行运动和形状特征提取，使用动态时间规整进行姿态序列分析，最后通过最近邻分类器进行分类。我们通过最常用的数据集评估了我们的系统，并获得了比现有方法更高的准确性。我们的系统还可以从人类动作序列查询中检索视频序列。

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