• This project aims to compenate skeleton data using Unscented Kalman Filter(UKF).
• Skeleton data is given 32 joint information from Kinect v2 camera and Azure Kinect Body Tracking SDK (v1.0.1).
• pykalman library is used for UKF
• python v3.7

1. install python 3
2. install python packages

All of these and pykalman can be install using easy_install

easy_install numpy matplotlib scipy wheel pykalman argparse

3. Download imagemagick to generate .gif file Download site: ImageMagick-7.0.10-10-Q16-x64-dll.exe install

git clone https://github.com/fbdp1202/pyukf_kinect_body_tracking.git
cd pyukf_kinect_body_tracking
python test.py jiwon crossing_arms_30sec

$.../pyukf_kinect_body_tracking>python test.py -h
usage: test.py [-h] [--filter {on,off}] [--model {ukf,kf}] [--plot {on,off}]
               [--num NUM] [--cbr_num CBR_NUM]
               First_string Second_string

positional arguments:
  First_string       What is your person name?
  Second_string      What is your pose? "*" is all pose

optional arguments:
  -h, --help         show this help message and exit
  --filter {on,off}  Do you want to plot?
  --model {ukf,kf}   which type of filter?
  --plot {on,off}    Do you want to plot?
  --num NUM          How many tests do you want?
  --cbr_num CBR_NUM  How many calibration tests do you want?

# Adjust the number of tests
python test.py jiwon crossing_arms_30sec --num 10

# off filter mode
python test.py jiwon crossing_arms_30sec --filter off

# off plot mode
python test.py jiwon crossing_arms_30sec --plot off

# one person, all pose test
python test.py jiwon *

# choose model 
# not yet implement kalman filter(kf) mode
# python test.py jiwon crossing_arms_30sec --model kf

• test_skeleton_filter(person_name, pose, test_num)

• The skeleton data folder looks like this:

  ./data/skeleton_data/pereson_name/pose/

• Skeleton data form named sk_timestamp_txt and include one person data

• save original and filtered data on ./result/person_name/pose/model_name/ each original_data.csv and estimate_data.csv

• Example code (persone_name="jiwon", pose_mode="crossing_arms_30sec", model="ukf")

test_skeleton_filter("jiwon", "crossing_arms_30sec", test_num=10, cbr_num=10, "ukf")

• test_skeleton_draw(person_name, pose, plot_3D)

• Load .csv file saved above to provide 3D plot, (x, y, z) point plot and joint length plot with pyplot.

• Folder where the point plot images and joint length plot images are saved is as follows:

  ./result/person_name/pose/mode_name/ each point and length folder

• point plot:

• length plot:

• 3D plot result:

	test_skeleton_draw("jiwon", "crossing_arms_30sec", "ukf")

• Finding better covariance values for UKF
• Displaying the combined depth image and filtered data
• Applying various filters such as Kalman Filter, Tobit kalman Filter, Particle Filter...
• Eliminate runtime error in UKF by applying rSVD rather than cholesky decomposition
• Linking with real-time applications
• Creating algorithms to cope with multi-skeleton data
• Automatic skeleton data classification
• Creating various disease diagnostic applications

MIT

Kinematic Data Filtering with Unscented Kalman Filter