Human pose estimation from video plays a critical role in various applications such as quantifying physical exercises, sign language recognition, and full-body gesture control.

Last update: Jan 17, 2022

Overview

Pose Detection

Project Description:

Human pose estimation from video plays a critical role in various applications such as quantifying physical exercises, sign language recognition, and full-body gesture control.

Requirements:

Following modules need to be installed for it to work properly:

Mediapipe
Cv2

OpenCV:

OpenCV is a huge open-source library for computer vision, machine learning, and image processing. OpenCV supports a wide variety of programming languages like Python, C++, Java, etc. It can process images and videos to identify objects, faces, or even the handwriting of a human.

It can be installed using "pip install opencv-python"

Mediapipe:

MediaPipe is a framework for building multimodal (eg. video, audio, any time series data), cross platform (i.e Android, iOS, web, edge devices) applied ML pipelines.

It can be installed using "pip install mediapipe"

Contact Me:

Owner

Hassan Shahzad

I am a passionate Software Developer who is always aspiring to learn more. Practice makes perfect and I am always working on improving my skills and myself.

GitHub Repository

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Human pose estimation from video plays a critical role in various applications such as quantifying physical exercises, sign language recognition, and full-body gesture control.

Related tags

Overview

Pose Detection

Project Description:

Requirements:

OpenCV:

Mediapipe:

Contact Me:

Owner

Hassan Shahzad

TSP: Temporally-Sensitive Pretraining of Video Encoders for Localization Tasks

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