A simple software for capturing human body movements using the Kinect camera.

Overview

KinectMotionCapture

Build Status DOI

A simple software for capturing human body movements using the Kinect camera. The software can seamlessly save joints and bones positions for further analysis.

Features

  • Compliance with one Kinect camera connected.
  • Tracking up to two people captured on the video stream.
  • Indicating by color which joints and bones are fully tracked or inferred.
  • Recording body movements of one person. Bones and joints positions data is saved to files.
  • Adjusting joint filtering options.
  • Saving screenshots of current video stream.

The application recognizes only joints and bones that were fully tracked or inferred by the Kinect camera. Tracked joints and bones with both of their joints tracked are indicated by green color, inferred joints and bones with only one of their joints tracked are indicated by yellow color, and bones with both of their joint inferred are indicated by red color.

Although being capable of tracking up to two skeletons on the video stream, the application saves all positions as if there was only one skeleton source. Therefore, if more than one skeleton is tracked, the user should indicate the main body to be captured by using the Set body function.

For a general overview of the Kinect skeletal tracking system please refer to [1].

Functions

Set body

The Set body function allows to choose the body to be captured (and its position saved) from all other bodies present on the video stream. To use this feature, the person to be captured must stand the closest to the camera, and then the Set body button must be clicked.

Kinect smoothing parameters

The skeletal tracking joint information can be adjusted across different frames to minimize jittering and stabilize the joint positions over time. This can be done by adjusting the smoothing parameters. A comprehensive description of these options can be found at [1].

Recording

Body movement can be recorded by clicking the Start recoding button. All data recorded is saved as comma-separated files in “data” folder in the root directory of the application. For the data file to be saved the Stop recording button must be clicked afterwards. Joints positions are saved as files named “<>-joint-<>.csv”. The files include data columns which contain timestamp of a measurement (timestamp), joint x position (x), joint y position (y), joint z position (z), and coordinate type (coord_type), which indicates whether the joint was fully tracked (1) or inferred (2). Bones positions are saved as files named “<>-bone-<>-<>.csv”. The files include data columns which contain timestamp of a measurement (timestamp), bone absolute rotation matrix (abs_m11 to abs_m44), bone absolute orientation in quaternion form (abs_x, abs_y, abs_z, and abs_w), bone hierarchical rotation matrix (h_m11 to h_m44), bone hierarchical orientation in quaternion form (h_x, h_y, h_z, and h_w), and coordinate type (coord_type), which indicates whether both joints of the bone were fully tracked (1), both were inferred (2) or only one of them was tracked (3).

Requirements

  • .NET Framework 4.5.2
  • Kinect for Windows SDK v1.8

References

[1] https://msdn.microsoft.com/en-us/library/hh973074.aspx

You might also like...
 SMPL-X: A new joint 3D model of the human body, face and hands together
SMPL-X: A new joint 3D model of the human body, face and hands together

SMPL-X: A new joint 3D model of the human body, face and hands together [Paper Page] [Paper] [Supp. Mat.] Table of Contents License Description News I

Face and Pose detector that emits MQTT events when a face or human body is detected and not detected.
Face and Pose detector that emits MQTT events when a face or human body is detected and not detected.

Face Detect MQTT Face or Pose detector that emits MQTT events when a face or human body is detected and not detected. I built this as an alternative t

Camera-caps - Examine the camera capabilities for V4l2 cameras
Camera-caps - Examine the camera capabilities for V4l2 cameras

camera-caps This is a graphical user interface over the v4l2-ctl command line to

 PoseViz – Multi-person, multi-camera 3D human pose visualization tool built using Mayavi.
PoseViz – Multi-person, multi-camera 3D human pose visualization tool built using Mayavi.

PoseViz – 3D Human Pose Visualizer Multi-person, multi-camera 3D human pose visualization tool built using Mayavi. As used in MeTRAbs visualizations.

CFC-Net: A Critical Feature Capturing Network for Arbitrary-Oriented Object Detection in Remote Sensing Images
CFC-Net: A Critical Feature Capturing Network for Arbitrary-Oriented Object Detection in Remote Sensing Images

CFC-Net This project hosts the official implementation for the paper: CFC-Net: A Critical Feature Capturing Network for Arbitrary-Oriented Object Dete

Towards Multi-Camera 3D Human Pose Estimation in Wild Environment
Towards Multi-Camera 3D Human Pose Estimation in Wild Environment

PanopticStudio Toolbox This repository has a toolbox to download, process, and visualize the Panoptic Studio (Panoptic) data. Note: Sep-21-2020: Curre

Python scripts for performing 3D human pose estimation using the Mobile Human Pose model in ONNX.
Python scripts for performing 3D human pose estimation using the Mobile Human Pose model in ONNX.

Python scripts for performing 3D human pose estimation using the Mobile Human Pose model in ONNX.

Self-Correcting Quantum Many-Body Control using Reinforcement Learning with Tensor Networks

Self-Correcting Quantum Many-Body Control using Reinforcement Learning with Tensor Networks This repository contains the code and data for the corresp

[CVPR2021] UAV-Human: A Large Benchmark for Human Behavior Understanding with Unmanned Aerial Vehicles

UAV-Human Official repository for CVPR2021: UAV-Human: A Large Benchmark for Human Behavior Understanding with Unmanned Aerial Vehicle Paper arXiv Res

Releases(v1.1)
Owner
Aleksander Palkowski
Aleksander Palkowski
Unofficial PyTorch implementation of Neural Additive Models (NAM) by Agarwal, et al.

nam-pytorch Unofficial PyTorch implementation of Neural Additive Models (NAM) by Agarwal, et al. [abs, pdf] Installation You can access nam-pytorch vi

Rishabh Anand 11 Mar 14, 2022
Online Pseudo Label Generation by Hierarchical Cluster Dynamics for Adaptive Person Re-identification

Online Pseudo Label Generation by Hierarchical Cluster Dynamics for Adaptive Person Re-identification

TANG, shixiang 6 Nov 25, 2022
An unofficial personal implementation of UM-Adapt, specifically to tackle joint estimation of panoptic segmentation and depth prediction for autonomous driving datasets.

Semisupervised Multitask Learning This repository is an unofficial and slightly modified implementation of UM-Adapt[1] using PyTorch. This code primar

Abhinav Atrishi 11 Nov 25, 2022
Source code for "Pack Together: Entity and Relation Extraction with Levitated Marker"

PL-Marker Source code for Pack Together: Entity and Relation Extraction with Levitated Marker. Quick links Overview Setup Install Dependencies Data Pr

THUNLP 173 Dec 30, 2022
I-BERT: Integer-only BERT Quantization

I-BERT: Integer-only BERT Quantization HuggingFace Implementation I-BERT is also available in the master branch of HuggingFace! Visit the following li

Sehoon Kim 139 Dec 27, 2022
Code accompanying paper: Meta-Learning to Improve Pre-Training

Meta-Learning to Improve Pre-Training This folder contains code to run experiments in the paper Meta-Learning to Improve Pre-Training, NeurIPS 2021. P

28 Dec 31, 2022
Source code for paper: Knowledge Inheritance for Pre-trained Language Models

Knowledge-Inheritance Source code paper: Knowledge Inheritance for Pre-trained Language Models (preprint). The trained model parameters (in Fairseq fo

THUNLP 31 Nov 19, 2022
(JMLR'19) A Python Toolbox for Scalable Outlier Detection (Anomaly Detection)

Python Outlier Detection (PyOD) Deployment & Documentation & Stats Build Status & Coverage & Maintainability & License PyOD is a comprehensive and sca

Yue Zhao 6.6k Jan 03, 2023
An open source library for face detection in images. The face detection speed can reach 1000FPS.

libfacedetection This is an open source library for CNN-based face detection in images. The CNN model has been converted to static variables in C sour

Shiqi Yu 11.4k Dec 27, 2022
🔥🔥High-Performance Face Recognition Library on PaddlePaddle & PyTorch🔥🔥

face.evoLVe: High-Performance Face Recognition Library based on PaddlePaddle & PyTorch Evolve to be more comprehensive, effective and efficient for fa

Zhao Jian 3.1k Jan 04, 2023
This code reproduces the results of the paper, "Measuring Data Leakage in Machine-Learning Models with Fisher Information"

Fisher Information Loss This repository contains code that can be used to reproduce the experimental results presented in the paper: Awni Hannun, Chua

Facebook Research 43 Dec 30, 2022
Predicting Price of house by considering ,house age, Distance from public transport

House-Price-Prediction Predicting Price of house by considering ,house age, Distance from public transport, No of convenient stores around house etc..

Musab Jaleel 1 Jan 08, 2022
CaLiGraph Ontology as a Challenge for Semantic Reasoners ([email protected]'21)

CaLiGraph for Semantic Reasoning Evaluation Challenge This repository contains code and data to use CaLiGraph as a benchmark dataset in the Semantic R

Nico Heist 0 Jun 08, 2022
Automated detection of anomalous exoplanet transits in light curve data.

Automatically detecting anomalous exoplanet transits This repository contains the source code for the paper "Automatically detecting anomalous exoplan

1 Feb 01, 2022
[AAAI2021] The source code for our paper 《Enhancing Unsupervised Video Representation Learning by Decoupling the Scene and the Motion》.

DSM The source code for paper Enhancing Unsupervised Video Representation Learning by Decoupling the Scene and the Motion Project Website; Datasets li

Jinpeng Wang 114 Oct 16, 2022
An OpenAI Gym environment for Super Mario Bros

gym-super-mario-bros An OpenAI Gym environment for Super Mario Bros. & Super Mario Bros. 2 (Lost Levels) on The Nintendo Entertainment System (NES) us

Andrew Stelmach 1 Jan 05, 2022
Open-Set Recognition: A Good Closed-Set Classifier is All You Need

Open-Set Recognition: A Good Closed-Set Classifier is All You Need Code for our paper: "Open-Set Recognition: A Good Closed-Set Classifier is All You

194 Jan 03, 2023
PyTorch implementation of Histogram Layers from DeepHist: Differentiable Joint and Color Histogram Layers for Image-to-Image Translation

deep-hist PyTorch implementation of Histogram Layers from DeepHist: Differentiable Joint and Color Histogram Layers for Image-to-Image Translation PyT

Winfried Lötzsch 10 Dec 06, 2022
Implementation based on Paper - Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling

Implementation based on Paper - Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling

HamasKhan 3 Jul 08, 2022
Self-Supervised Monocular DepthEstimation with Internal Feature Fusion(arXiv), BMVC2021

DIFFNet This repo is for Self-Supervised Monocular Depth Estimation with Internal Feature Fusion(arXiv), BMVC2021 A new backbone for self-supervised d

Hang 94 Dec 25, 2022