Human Activity Detection with TensorFlow and Python.
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Updated
Apr 27, 2024 - Python
Human Activity Detection with TensorFlow and Python.
Human Pose Estimation for multi-view Human Action Recognition
Implementation of CNN-Based Model for Online Action Recognition
This python opencv code is used to segment the human object from the video frame
This is an effort to provide different approaches towards human action recognition from video. A method to perform data augmentation on skeletal data so as to achieve a view independent recognition approach is included.
[ECCV 2024]Temporary code for "Ad-HGformer: An Adaptive HyperGraph Transformer for Skeletal Action Recognition"
Thesis, Video Based Human Action Recognition Using Deep Learning
Striking the Balance: Human Pose Estimation based Optimal Fall Recognition
A system for Human Action Recognition that uses the scale and body orientation invariant Skeletal Quads representation, with an LSTM network
Source code of experiments performed in paper: Human Action Recognition in Videos Based on Spatiotemporal Features and Bag-of-Poses
[AAAI-2024] HARDVS: Revisiting Human Activity Recognition with Dynamic Vision Sensors
Human Activity Recognition Research Repository
A human action dataset collected from Elder Scrolls V: Skyrim
Implementation of some popular skeleton based Human Action Recognition methods basis on Deep Neural Networks.
This repository provides implementation of a baseline method and our proposed methods for efficient Skeleton-based Human Action Recognition.
Code for HAR-GCNN: Deep Graph CNNs for Human Activity Recognition From Highly Unlabeled Mobile Sensor Data, IEEE PerCom CoMoRea 2022
A skeleton-based real-time online action recognition project, classifying and recognizing base on framewise joints, which can be used for safety surveilence.
This repository contains the MPOSE2021 Dataset for short-time pose-based Human Action Recognition (HAR).
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.
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