Triggered by the advent of social media, human pose estimation has gained traction in various applications such as gaming, activity recognition, gaming and augmented reality. In recent years, there has been a multitude of new models released, leveraging on deep learning networks as backbone (ResNet, MobileNet, VGG etc) and trained on datasets like COCO, MPII and Body25.

It is confusing for many starting out on real-time pose estimation to select an approach. This article outlines considerations when selecting the model to utilize.

1. Define Purpose

The first over-riding factor to ponder upon is purpose of project you are embarking on. …


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