论文标题
在活动识别和姿势估计数据集上的不同机器学习方法的性能
Performance of different machine learning methods on activity recognition and pose estimation datasets
论文作者
论文摘要
随着计算机视觉的进步日常发生，最近在活动识别方面散发出了很多光线。随着利用这一研究领域的现实应用程序范围在安全和医疗保健等众多行业中的增加，对于企业区分哪种机器学习方法的性能比该地区的其他机器更好。本文致力于帮助这种困境，即基于以前的相关工作，它在丰富的姿势估计（OpenPose）和HAR数据集上采用了古典和合奏方法。利用适当的指标来评估每个模型的性能，结果表明，整体，随机森林在分类ADL中的准确性最高。相对而言，所有模型在两个数据集中均具有出色的性能，除了逻辑回归和Adaboost在HAR ONE中的表现不佳。最终还讨论了本文的局限性，进一步研究的范围是广泛的，可以将本文用作产生更好结果的目标的基础。
With advancements in computer vision taking place day by day, recently a lot of light is being shed on activity recognition. With the range for real-world applications utilizing this field of study increasing across a multitude of industries such as security and healthcare, it becomes crucial for businesses to distinguish which machine learning methods perform better than others in the area. This paper strives to aid in this predicament i.e. building upon previous related work, it employs both classical and ensemble approaches on rich pose estimation (OpenPose) and HAR datasets. Making use of appropriate metrics to evaluate the performance for each model, the results show that overall, random forest yields the highest accuracy in classifying ADLs. Relatively all the models have excellent performance across both datasets, except for logistic regression and AdaBoost perform poorly in the HAR one. With the limitations of this paper also discussed in the end, the scope for further research is vast, which can use this paper as a base in aims of producing better results.
