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

Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing, Ant Colony Optimization Algorithm,Immune Algorithm, Artificial Fish Swarm Algorithm, Differential Evolution and TSP(Traveling salesman)

🔥 TensorFlow Code for technical report: "YOLOv3: An Incremental Improvement"

The implementation for "Comprehensive Knowledge Distillation with Causal Intervention".

ISTR: End-to-End Instance Segmentation with Transformers (https://arxiv.org/abs/2105.00637)

[Open Source]. The improved version of AnimeGAN. Landscape photos/videos to anime

Sleep staging from ECG, assisted with EEG

Denoising images with Fourier Ring Correlation loss

Convert openmmlab (not only mmdetection) series model to tensorrt

TilinGNN: Learning to Tile with Self-Supervised Graph Neural Network (SIGGRAPH 2020)

Analyzing basic network responses to novel classes

Code and models for "Pano3D: A Holistic Benchmark and a Solid Baseline for 360 Depth Estimation", OmniCV Workshop @ CVPR21.

Implementation of various Vision Transformers I found interesting

Improving Query Representations for DenseRetrieval with Pseudo Relevance Feedback:A Reproducibility Study.

Code for T-Few from "Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning"

This is an example of a reproducible modelling project

A Pytorch reproduction of Range Loss, which is proposed in paper 《Range Loss for Deep Face Recognition with Long-Tailed Training Data》

Unsupervised Learning of Video Representations using LSTMs

Project page of the paper 'Analyzing Perception-Distortion Tradeoff using Enhanced Perceptual Super-resolution Network' (ECCVW 2018)

Xview3 solution - XView3 challenge, 2nd place solution

Algorithm to texture 3D reconstructions from multi-view stereo images