Multi-Person 2D Pose Estimation

Source: Deep Learning on Medium

Multi-Person 2D Pose Estimation

This is very interesting → in real-time multiple people pose tracking. (new representation of encoding → this is very sexy).

Very good → the lighting is not good for the demo but it really worked very well. (realtime we need a lot of computational power → how can we achieved this?).

Heatmap is multiple one → this is quite challenging. (is this problem solved? → this is no → we need to optimize the key part into people → each people).

Efficient representation → was the key idea.

The direction vector → is there → this is related to the optimal flow → position and the orientation of the human body parts.

So we are encoding the connection → between different body parts. (we need both direction encoding) (point to one body part from another).

Hence the representation can be looked like that → remove the incorrect parts.

This research → relates to graph theory. (the nodes are now going to be optimized to fit some certain model).

There are different branches → very good → and this is the iterative method → training was hard but really did well. (this became open-pose).

This became realtime → open-pose → there is a complicated post-processing step in the prediction.

Multi-pose is much harder to optimize.

Some approaches → are good and mode efficient.

Very good implementation and quite complicated preprocessing step.

There are multiple stages → quite hard to optimize.

Holly shit the post-processing step is super complicated → so much preprocessing has to be done.

There is another network called faster RCNN. (pyramid network is the backbone).

Wow, quite complicated architecture.