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首页> 外文期刊>Pattern Recognition: The Journal of the Pattern Recognition Society >Exploring trace transform for robust human action recognition
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Exploring trace transform for robust human action recognition

机译:探索轨迹转换以实现可靠的人体动作识别

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摘要

Machine based human action recognition has become very popular in the last decade. Automatic unattended surveillance systems, interactive video games, machine learning and robotics are only few of the areas that involve human action recognition. This paper examines the capability of a known transform, the so-called Trace, for human action recognition and proposes two new feature extraction methods based on the specific transform. The first method extracts Trace transforms from binarized silhouettes, representing different stages of a single action period. A final history template composed from the above transforms, represents the whole sequence containing much of the valuable spatio-temporal information contained in a human action. The second, involves Trace for the construction of a set of invariant features that represent the action sequence and can cope with variations usually appeared in video capturing. The specific method takes advantage of the natural specifications of the Trace transform, to produce noise robust features that are invariant to translation, rotation, scaling and are effective, simple and fast to create. Classification experiments performed on two well known and challenging action datasets (KTH and Weizmann) using Radial Basis Function (RBF) Kernel SVM provided very competitive results indicating the potentials of the proposed techniques.
机译:在过去的十年中,基于机器的人类动作识别已经非常流行。自动无人值守监视系统,交互式视频游戏,机器学习和机器人技术只是涉及人类行为识别的领域中的少数领域。本文研究了已知变换(所谓的跟踪)对人类动作识别的功能,并提出了基于特定变换的两种新的特征提取方法。第一种方法从二值化轮廓提取跟踪变换,表示单个动作周期的不同阶段。由上述变换组成的最终历史模板代表了整个序列,其中包含人类行为中包含的许多宝贵的时空信息。第二,涉及跟踪,用于构造代表动作序列的一组不变特征,并可以应对视频捕获中通常出现的变化。该特定方法利用了Trace变换的自然规格,以产生噪声稳健的特征,这些特征对于平移,旋转,缩放不变,并且有效,简单且快速地创建。使用径向基函数(RBF)内核SVM在两个众所周知的具有挑战性的动作数据集(KTH和Weizmann)上进行的分类实验提供了非常有竞争力的结果,表明了所提出技术的潜力。

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