human-action-recognition

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

deep learning sex position classifier

Human action classification system with pose-based (MediaPipe) and video-based (3D CNN) models. Features 100+ architectures for real-time pose classification and temporal models pretrained on UCF-101/HMDB51.

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, IJCV-2026] HARDVS: Revisiting Human Activity Recognition with Dynamic Vision Sensors

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.

[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

MSR Action Recognition Datasets and Codes

A real-time inferencing of multistreaming YOWOv3(Spatio Temporal Action Detection task) using (UCF101-24) dataset. The repo is extension of https://github.com/Hope1337/YOWOv3, https://arxiv.org/pdf/2408.02623 https://github.com/dilwolf/YOWOv3-Improved/tree/main

WiFi CSI human sensing for Python presence, fall detection, breathing rate, activity classification, and occupancy counting from an ESP32. No camera, no cloud, no PyTorch/CUDA at runtime.

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