human-action-recognition

Real-Time Spatio-Temporally Localized Activity Detection by Tracking Body Keypoints

deep learning sex position classifier

This repository allows you to classify 40 different human actions. Pose detection, estimation and classification is also performed. Poses are classified into sitting, upright and lying down.

Keras implementation of Human Action Recognition for the data set State Farm Distracted Driver Detection (Kaggle)

Source code for "Learning Graph Convolutional Network for Skeleton-based Human Action Recognition by Neural Searching", AAAI2020

Surveillance Perspective Human Action Recognition Dataset: 7759 Videos from 14 Action Classes, aggregated from multiple sources, all cropped spatio-temporally and filmed from a surveillance-camera like position.

A Comprehensive Tutorial on Video Modeling

This repository contains the MPOSE2021 Dataset for short-time pose-based Human Action Recognition (HAR).

[AAAI-2024] HARDVS: Revisiting Human Activity Recognition with Dynamic Vision Sensors

[TPAMI 2020] "Privacy-Preserving Deep Action Recognition: An Adversarial Learning Framework and A New Dataset" by Zhenyu Wu, Haotao Wang, Zhaowen Wang, Hailin Jin, and Zhangyang Wang

Computer Vision Project : Action Recognition on UCF101 Dataset

This repository provides implementation of a baseline method and our proposed methods for efficient Skeleton-based Human Action Recognition.

MSR Action Recognition Datasets and Codes

Activity Recognition using Temporal Optical Flow Convolutional Features and Multi-Layer LSTM

Deep learning model that predicts human action in a given video feed using pose estimation

机器学习实现基于手机六轴数据的人体动作识别和计数功能。并利用云服务器和微信小程序在手机上实现。 Use machine learning to achieve human activity recognition and counting function based on cell phone six-axis data. Achieve it on phone using ECS and WeChat mini-program.

Human Activity Recognition Research Repository

Code for HAR-GCNN: Deep Graph CNNs for Human Activity Recognition From Highly Unlabeled Mobile Sensor Data, IEEE PerCom CoMoRea 2022

This includes a novel method to measure the quality of the actions performed in Olympic weightlifting using human action recognition in videos. Human action recognition is a well-studied problem in computer vision and on the other hand action quality assessment is researched and experimented comparatively low. This is due to the lack of datasets that can be used to assess the quality of actions. In this research, we introduce a method to assess player techniques in weightlifting by using skeleton-based human action recognition. Furthermore, we introduce a new video dataset for action recognition in weightlifting which is annotated to frame level. We intended to develop a viable automated scoring system through action recognition that would be beneficial in the sports industry.

Implementation of some popular skeleton based Human Action Recognition methods basis on Deep Neural Networks.