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