Abstract Human action recognition in videos is a tough task due to the complex background, geometrical transformation and an enormous volume of data. Hence, to address these issues, an effective algorithm is developed, which can identify human action in videos using a single decisive pose. To achieve the task, a decisive pose is extracted using optical flow, and further, feature extraction is done via a two-fold transformation of wavelet. The two-fold transformation is done via Gabor Wavelet Transform (GWT) and Ridgelet Transform (RT). The GWT produces a feature vector by calculating first-order statistics values of different scale and orientations of an input pose, which have robustness against translation, scaling and rotation. The orientation-dependent shape characteristics of human action are computed using RT. The fusion of these features gives a robust unified algorithm. The effectiveness of the algorithm is measured on four publicly datasets i.e. KTH, Weizmann, Ballet Movement, and UT Interaction and accuracy reported on these datasets are 96.66%, 96%, 92.75% and 100%, respectively. The comparison of accuracies with similar state-of-the-arts shows superior performance.

中文翻译：

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使用决定性姿势的人类行为识别的二重转换模型

摘要 由于背景复杂、几何变换和数据量巨大，视频中的人体动作识别是一项艰巨的任务。因此，为了解决这些问题，开发了一种有效的算法，该算法可以使用单个决定性姿势识别视频中的人类动作。为了完成任务，使用光流提取决定性姿势，并且进一步通过小波的二次变换完成特征提取。二次变换是通过 Gabor 小波变换 (GWT) 和 Ridgelet 变换 (RT) 完成的。GWT 通过计算输入姿态的不同尺度和方向的一阶统计值来产生特征向量，该值具有抗平移、缩放和旋转的鲁棒性。人类动作的方向相关形状特征是使用 RT 计算的。这些特征的融合提供了一个强大的统一算法。该算法的有效性是在四个公开数据集上测量的，即 KTH、魏茨曼、芭蕾舞运动和 UT 交互，在这些数据集上报告的准确率分别为 96.66%、96%、92.75% 和 100%。将精度与类似的最新技术进行比较显示出卓越的性能。