Gestures are one of the basic modes of human communication and are usually used to represent different actions. Automatic recognition of these actions forms the basis for solving more complex problems like human behavior analysis, video surveillance, event detection, and sign language recognition, etc. Action recognition from images is a challenging task as the key information like temporal data, object trajectory, and optical flow are not available in still images. While measuring the size of different regions of the human body i.e., step size, arms span, length of the arm, forearm, and hand, etc., provides valuable clues for identification of the human actions. In this article, a framework for classification of the human actions is presented where humans are detected and localized through faster region-convolutional neural networks followed by morphological image processing techniques. Furthermore, geometric features from human blob are extracted and incorporated into the classification rules for the six human actions i.e., standing, walking, single-hand side wave, single-hand top wave, both hands side wave, and both hands top wave. The performance of the proposed technique has been evaluated using precision, recall, omission error, and commission error. The proposed technique has been comparatively analyzed in terms of overall accuracy with existing approaches showing that it performs well in contrast to its counterparts.

中文翻译：

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使用人体几何和尺寸比例科学进行动作分类的图像分析

手势是人类交流的基本方式之一，通常用于表示不同的动作。这些动作的自动识别是解决诸如人类行为分析，视频监视，事件检测和手语识别等更复杂问题的基础。图像的动作识别是一项具有挑战性的任务，因为它是诸如时态数据，物体轨迹，和光流在静止图像中不可用。在测量人体不同区域的大小时，即步长，手臂跨度，手臂长度，前臂和手等，为识别人体行为提供了有价值的线索。在这篇文章中，提出了一种对人类行为进行分类的框架，其中通过更快的区域卷积神经网络以及随后的形态图像处理技术对人类进行检测和定位。此外，从人类斑点中提取几何特征并将其并入到六个人体动作的分类规则中，即站立，行走，单手侧波，单手顶波，双手侧波和双手顶波。已使用精度，召回率，遗漏误差和委托误差评估了所提出技术的性能。所提出的技术已经通过现有方法从总体准确性方面进行了比较分析，显示出与同类技术相比，其性能良好。提取人类斑点的几何特征并将其合并到六个人类动作的分类规则中，即站立，行走，单手侧波，单手顶波，双手侧波和双手顶波。已使用精度，召回率，遗漏误差和委托误差评估了所提出技术的性能。所提出的技术已经通过现有方法从总体准确性方面进行了比较分析，显示出与同类技术相比，其性能良好。提取人类斑点的几何特征并将其合并到六个人类动作的分类规则中，即站立，行走，单手侧波，单手顶波，双手侧波和双手顶波。已使用精度，召回率，遗漏误差和委托误差评估了所提出技术的性能。所提出的技术已经通过现有方法从总体准确性方面进行了比较分析，显示出与同类技术相比，其性能良好。