Performance of different machine learning methods on activity recognition and pose estimation datasets

TL;DR

This study compares various machine learning methods, including classical and ensemble approaches, on activity recognition and pose estimation datasets, finding that random forest generally outperforms others in accuracy.

Contribution

It evaluates multiple machine learning models on pose estimation and activity recognition datasets, providing insights into their relative performance and identifying the most effective approaches.

Findings

Random forest achieves the highest accuracy in classifying activities.

Most models perform well across datasets, except logistic regression and AdaBoost.

The paper discusses limitations and future research directions.

Abstract

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.