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

arXiv: https://arxiv.org/abs/2210.10247 · HTML: https://mlsanity.com/paper/2210.10247/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 31 references that are in the map.
- First posted on arXiv 19 Oct 2022.

## Builds on (most cited first, 13 of 13)

- [Stacked Hourglass Networks for Human Pose Estimation](https://mlsanity.com/paper/1603.06937/) (2016)
- [DeepPose: Human Pose Estimation via Deep Neural Networks](https://mlsanity.com/paper/1312.4659/) (2013)
- [Human Pose Estimation with Iterative Error Feedback](https://mlsanity.com/paper/1507.06550/) (2015)
- [DeepCut: Joint Subset Partition and Labeling for Multi Person Pose Estimation](https://mlsanity.com/paper/1511.06645/) (2015)
- [DeeperCut: A Deeper, Stronger, and Faster Multi-Person Pose Estimation Model](https://mlsanity.com/paper/1605.03170/) (2016)
- [Long-term Temporal Convolutions for Action Recognition](https://mlsanity.com/paper/1604.04494/) (2016)
- [Multi-Context Attention for Human Pose Estimation](https://mlsanity.com/paper/1702.07432/) (2017)
- [Learning Feature Pyramids for Human Pose Estimation](https://mlsanity.com/paper/1708.01101/) (2017)
- [Human Pose Regression by Combining Indirect Part Detection and Contextual Information](https://mlsanity.com/paper/1710.02322/) (2017)
- [Glimpse Clouds: Human Activity Recognition from Unstructured Feature Points](https://mlsanity.com/paper/1802.07898/) (2018)
- [Knowledge-Guided Deep Fractal Neural Networks for Human Pose Estimation](https://mlsanity.com/paper/1705.02407/) (2017)
- [Body Joint guided 3D Deep Convolutional Descriptors for Action Recognition](https://mlsanity.com/paper/1704.07160/) (2017)
- [Self Adversarial Training for Human Pose Estimation](https://mlsanity.com/paper/1707.02439/) (2017)

## Abstract (arXiv)

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.