A two-fold transformation model for human action recognition using decisive pose
A two-fold transformation model for human action recognition using decisive pose
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DOI：
10.1016/j.cogsys.2019.12.004
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发表时间：
2020-06-01
影响因子：
3.9
通讯作者：
Vishwakarma, Dinesh Kumar
中科院分区：
文献类型：
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作者：
Vishwakarma, Dinesh Kumar
视频中的人体动作识别由于背景复杂、几何变换和数据量大，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行为。为了实现该任务，利用光流提取决定性姿态，然后通过小波的双重变换进行特征提取。通过Gabor小波变换（GWT）和脊波变换（RT）进行二次变换。GWT通过计算输入姿态的不同尺度和方向的一阶统计值产生特征向量，该特征向量对平移、缩放和旋转具有鲁棒性。利用rt计算人体动作的方向相关形状特征，将这些特征融合在一起，得到鲁棒的统一算法。算法的有效性在KTH、Weizmann、Ballet Movement和UT Interaction四个公开数据集上进行了测试，这些数据集上的准确率分别为96.66%、96%、92.75%和100%。与同类先进技术的精度比较显示出优越的性能。(C) 2019 Elsevier B.V.版权所有
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 twofold 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. (C) 2019 Elsevier B.V. All rights reserved.