Pose Estimation using Computer Vision
What is Computer Vision?
Computer vision is a field of artificial intelligence (AI) that enables computers and systems to derive meaningful information from digital images, videos and other visual inputs — and take actions or make recommendations based on that information. If AI enables computers to think, computer vision enables them to see, observe and understand.
Computer vision works much the same as human vision, except humans have a head start. Human sight has the advantage of lifetimes of context to train how to tell objects apart, how far away they are, whether they are moving and whether there is something wrong in an image.
Computer vision trains machines to perform these functions, but it has to do it in much less time with cameras, data and algorithms rather than retinas, optic nerves and a visual cortex. Because a system trained to inspect products or watch a production asset can analyze thousands of products or processes a minute, noticing imperceptible defects or issues, it can quickly surpass human capabilities.
How does computer vision work?
Computer vision needs lots of data. It runs analyses of data over and over until it discerns distinctions and ultimately recognize images. For example, to train a computer to recognize automobile tires, it needs to be fed vast quantities of tire images and tire-related items to learn the differences and recognize a tire, especially one with no defects.
Two essential technologies are used to accomplish this: a type of machine learning called deep learning and a convolutional neural network (CNN).
Machine learning uses algorithmic models that enable a computer to teach itself about the context of visual data. If enough data is fed through the model, the computer will “look” at the data and teach itself to tell one image from another. Algorithms enable the machine to learn by itself, rather than someone programming it to recognize an image.
A CNN helps a machine learning or deep learning model “look” by breaking images down into pixels that are given tags or labels. It uses the labels to perform convolutions (a mathematical operation on two functions to produce a third function) and makes predictions about what it is “seeing.” The neural network runs convolutions and checks the accuracy of its predictions in a series of iterations until the predictions start to come true. It is then recognizing or seeing images in a way similar to humans.
What is Pose Estimation?
Pose estimation is a computer vision task that infers the pose of a person or object in an mage or video. We can also think of pose estimation as the problem of determining the position and orientation of a camera relative to a given person or object.
Recommended by LinkedIn
This is typically done by identifying, locating, and tracking a number of keypoints on a given object or person. For objects, this could be corners or other significant features. And for humans, these keypoints represent major joints like an elbow or knee.
The goal of our machine learning models are to track these keypoints in images and videos.
Why does pose estimation matter?
With pose estimation, we’re able to track an object or person (or multiple people, as we’ll discuss shortly) in real-world space at an incredibly granular level. This powerful capability opens up a wide range of possible applications.
Pose estimation also differs from other common computer vision tasks in some important ways. A task like object detection also locates objects within an image. This localization, though, is typically coarse-grained, consisting of a bounding box encompassing the object. Pose estimation goes further, predicting the precise location of keypoints associated with the object.
We can clearly envision the power of pose estimation by considering its application in automatically tracking human movement. From virtual sports coaches and AI-powered personal trainers to tracking movements on factory floors to ensure worker safety, pose estimation has the potential to create a new wave of automated tools designed to measure the precision of human movement.
In addition to tracking human movement and activity, pose estimation opens up applications in a range of areas, such as:
References:-
