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INTERFACE
WORKFLOW
Skeletonographer:
Skeleton-based
Digital Ethnography Tool
Skeletonographer is a web-based digital ethnography
tool that supports researchers in analyzing human be-
haviours by using anonymous skeletonized representa-
tions. It generates skeleton dataframe-by-frame from
video input using computer vision results from the Open-
Pose library.
SKELETONOGRAPHERMOTIVATION
Bokyung Lee*, Michael Lee, Pan Zhang, Alexander Tessier, Daniel Saakes*, Azam Khan
*
Despite the richness and reliability of in-the-wild data, re-
search ethics in terms of privacy makes it challenging to
collect, save and analyze data from the field. In this proj-
ect, we aim to support anonymous digital ethnogra-
phy studies which include rich and realistic human in-
teractions.
input
input
output
• visualize human activities with skeletons
• playback skeletonized human activities
• annotate activitiesMP4 data
(frame, timestamp)
JSON data
(timestamp,
+ joint positions)
f0f1
fn
. . .
t
JSONday
JSONframe
OpenPose
(25 joints)
f0
f1
fn
t
Annotation
+ Timestamp
A.source panel (drop-down menu) B. label panel E.video control panelCAMERA 03 2019. 01. 30
C. video panel
D.timeline panel
D-6) people
graph
D-5) timeline
disable
skeleton view
control video
D-7) label bars
D-2) time control bar
D-3) frame number, time (GDT)
D-1)
target area
for labeling
A-1) camera source
A-2) date
C-1) anonymized
skeletons
Skeleton Mode
Player
D-4) labeled results
The workflow is composed of three steps: 1) generating 2D skeletons from videos using computer vision, 2) manual annotation
of ethnographic data, 3) exporting data sets which include the type of activity, relevant skeletons, and timestamps as JSON.
The interface is composed of four panels: a) source panel, b) video panel,
c) label panel and d) timeline panel.

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Skeletonographer: Skeleton-based Digital Ethnography Tool

  • 1. INTERFACE WORKFLOW Skeletonographer: Skeleton-based Digital Ethnography Tool Skeletonographer is a web-based digital ethnography tool that supports researchers in analyzing human be- haviours by using anonymous skeletonized representa- tions. It generates skeleton dataframe-by-frame from video input using computer vision results from the Open- Pose library. SKELETONOGRAPHERMOTIVATION Bokyung Lee*, Michael Lee, Pan Zhang, Alexander Tessier, Daniel Saakes*, Azam Khan * Despite the richness and reliability of in-the-wild data, re- search ethics in terms of privacy makes it challenging to collect, save and analyze data from the field. In this proj- ect, we aim to support anonymous digital ethnogra- phy studies which include rich and realistic human in- teractions. input input output • visualize human activities with skeletons • playback skeletonized human activities • annotate activitiesMP4 data (frame, timestamp) JSON data (timestamp, + joint positions) f0f1 fn . . . t JSONday JSONframe OpenPose (25 joints) f0 f1 fn t Annotation + Timestamp A.source panel (drop-down menu) B. label panel E.video control panelCAMERA 03 2019. 01. 30 C. video panel D.timeline panel D-6) people graph D-5) timeline disable skeleton view control video D-7) label bars D-2) time control bar D-3) frame number, time (GDT) D-1) target area for labeling A-1) camera source A-2) date C-1) anonymized skeletons Skeleton Mode Player D-4) labeled results The workflow is composed of three steps: 1) generating 2D skeletons from videos using computer vision, 2) manual annotation of ethnographic data, 3) exporting data sets which include the type of activity, relevant skeletons, and timestamps as JSON. The interface is composed of four panels: a) source panel, b) video panel, c) label panel and d) timeline panel.