'What is Human Pose Estimation?' by ITRexGroup computervision artificialintelligence
Such rapid growth in demand has marked the time for human pose estimation technology to step into widespread commercial use. In this article, we will look under the hood of human pose estimation technology, explore its use case, and share tips fromIt is easy for a human to tell whether a person is squatting down or waving a hand. For a machine? Not so simple. When shown a photo or a video frame, a computer “sees” a collection of pixels.
A standard deep learning model for human pose estimation has a convolutional neural network at its base. And the CNN’s architecture comprises two key components — an encoder and a decoder . For multi-person estimation, when there are several high-probability areas for each keypoint, say, two left elbows, an additional post-processing layer may be added to ensure each keypoint belongs to the right person.
MoveNet is an open-source library for 3D pose estimation that detects 17 keypoints. The library comes in two variants: Lightning and Thunder. The former is intended for latency-critical applications, while the latter is appropriate for apps that require high accuracy. Both Lightning and Thunder run faster than in real time on desktop, laptop, and mobile devices.
In interactive video gaming, human pose estimation is used to capture players’ motions and render them into the actions of virtual characters as well., retailers have started turning to AR and real-time virtual effects. Human pose estimation backs up those aspirations, enabling such experiences as virtual try-on and real-time marketing.
In turn, if you are developing a product with a mass-market appeal or a product that caters to a typical use case, going for library-based development would help build a quality prototype faster and with lower effort. Still, in many cases, you would have to adjust the given model to your specific use case by further training it on the data that best reflects the reality.
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