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Traditional Features based Automated System for Human Activities Recognition

机译：基于传统特征的人类活动自动识别系统

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摘要

Human Activities Recognition (HAR) is an important research topic and its applications are spread in all the fields of computer vision and machine learning including video surveillance, robotics, and name a few more. In this paper, a new traditional feature fusion and selection-based method is proposed for automated HAR. The proposed methodology consists of three core steps- optical flow-based motion region extraction and later ROI detection, shape and gray level difference matrix (GLDM) features are combined in one matrix based on seniority value indexes, and finally, Reyni entropy-controlled Euclidean classifier based best features selection. The final selected features are put to Cubic SVM for final recognition. The validation of the proposed technique is conducted on three datasets- KTH, YouTube, and Weizmann and achieved an accuracy of 99.30%, 99.80%, and 99.60%, respectively. Overall, Cubic SVM outperforms among existing techniques.
机译：人类活动识别（HAR）是一个重要的研究主题，其应用遍及计算机视觉和机器学习的所有领域，包括视频监视，机器人技术等。本文提出了一种新的基于传统特征融合和选择的自动HAR方法。所提出的方法包括三个核心步骤：基于光流的运动区域提取和后来的ROI检测，形状和灰度差异矩阵（GLDM）特征根据优先级值指标组合到一个矩阵中，最后是Reyni熵控制的欧几里得基于分类器的最佳功能选择。最终选择的功能将放入Cubic SVM中以进行最终识别。对三个数据集（KTH，YouTube和Weizmann）进行了所提出技术的验证，其准确度分别为99.30％，99.80％和99.60％。总体而言，在现有技术中，Cubic SVM的性能优于其他技术。

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