Pose Estimation is a general problem in Computer Vision where we detect the position and orientation of an object. This usually means detecting keypoint locations that describe the object.
First, install all the necessary libraries.
– pip install OpenCV-python
The COCO keypoints include 17 different pre-trained keypoints (classes) that are annotated with three values (x,y,v). The x and y values mark the coordinates, and v indicates the visibility of the key point (visible, not visible). While image recognition systems usually perform well on such iconic views, they struggle to recognize objects in real-life scenes that show a complex scene or partially occlude the object. Hence, it is an essential aspect of the coco images that they contain natural images that contain multiple objects.
Human pose estimation refers to the process of inferring poses in an image. Essentially, it entails predicting the positions of a person’s joints in an image or video. This problem is also sometimes referred to as the localization of human joints. It’s also important to note that pose estimation has various sub-tasks such as single pose estimation, estimating poses in an image with many people, estimating poses in crowded places, and estimating poses in videos.
