Welcome to JetCobot repository

Human pose estimation

Code path: ~/jetcobot_ws/src/jetcobot_visual/detection

1.Overview

Human Posture Estimation estimates human posture by correctly linking the key points of the human body detected in the image. The key points of the human body usually correspond to joints with a certain degree of freedom on the human body, such as the neck, shoulder, elbow, wrist, waist, knee, ankle, etc., as shown in the figure below.

2. Principle

Input an image, extract features through the convolutional network, get a set of feature maps, and then split into two branches, using the CNN network to extract Part Confidence Maps and Part Affinity Fields respectively.

After obtaining these two pieces of information, we use Bipartite Matching in graph theory to find Part Association, connect the joints of the same person, and because of the vector nature of PAF itself, the generated bipartite matching is very correct, and finally merged into a person's overall skeleton.

Finally, based on PAFs, find Multi-Person Parsing—>Convert the Multi-person parsing problem into a graphs problem—>Hungarian Algorithm

(The Hungarian algorithm is the most common algorithm for partial graph matching. The core of the algorithm is to find augmenting paths. It is an algorithm that uses augmenting paths to find the maximum matching of bipartite graphs.)

3. Start

cd ~/jetcobot_ws/src/jetcobot_visual/detection
python3 target_openpose.py

Input picture

Output picture

You can see the actual footage captured by the camera.