Evaluation of feature selection on human activity recognition

Hussein Mazaar, E. Emary, H. Onsi
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引用次数: 8

Abstract

The paper presents an approach for feature selection in human activity recognition. Features are extracted based on spatiotemporal orientation energy and activity template, while feature reduction has been studied thoroughly using various techniques. Due to high dimensional data from extraction phase, a model with less features which are important and significant can build attractive, interpretative and accurate model. Finally, activity classification is done using SVM. With experiments to classify six activities of the KTH Dataset, significant feature reductions were reported with optimal embedded selection recorded for Gradient Boosting and R-Square techniques. The results show a reduction in time and improvement in accuracy. The Comparison to related work were given.
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特征选择在人体活动识别中的评价
提出了一种人体活动识别中的特征选择方法。特征提取主要基于时空方向能和活动模板,而特征约简则采用多种技术进行了深入研究。由于提取阶段的数据是高维的,用较少的重要和有意义的特征来构建具有吸引力、可解释性和准确性的模型。最后,利用支持向量机对活动进行分类。通过对KTH数据集的六个活动进行分类的实验,报告了显著的特征减少,并记录了梯度增强和r平方技术的最佳嵌入选择。结果表明,该方法缩短了时间,提高了精度。并与相关工作进行了比较。
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