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Human Activity Recognition Based on Weighted Sum Method and Combination of Feature Extraction Methods

机译：基于加权和与特征提取相结合的人类活动识别

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Human Activity Recognition (HAR) is one of the most important areas of computer vision research. The biggest difficulty for HAR system is that the camera could only film in one direction, leading to a shortage of data and low recognition results. This paper focuses on researching and building new models of HAR, including Principal Components Analysis (PCA), Linear discriminant Analysis (LDA) is to reduce the dimensionality and size of data, contributing to high recognition accuracy. First, from the 3D motion data, we conducted a pretreatment and feature extraction of objects. Next, we built a recognition model corresponding to each feature extraction method and we used Support Vector Machine (SVM) model to train. Finally, we used weighted methods to combine the results of the model to train and give the final results. The paper experiment on CMU MOCAP database and the percentage receiving proposed method is higher than that from the previous method.
机译：人类活动识别（HAR）是计算机视觉研究的最重要领域之一。 HAR系统的最大困难是相机只能在一个方向上拍摄，从而导致数据不足和识别结果低下。本文着重研究和建立HAR的新模型，包括主成分分析（PCA），线性判别分析（LDA）旨在减小数据的维数和大小，有助于提高识别精度。首先，我们从3D运动数据中进行了对象的预处理和特征提取。接下来，我们建立了与每种特征提取方法相对应的识别模型，并使用支持向量机（SVM）模型进行训练。最后，我们使用加权方法来组合模型的结果以进行训练并给出最终结果。本文在CMU MOCAP数据库上进行了实验，提出的接收比例高于以前的方法。

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