Publikation
Learning Task Structure from Video Examples for Workflow Tracking and Authoring
Nils Petersen; Didier Stricker
In: 11th IEEE International Symposium on Mixed and Augmented Reality (ISMAR), 2012. IEEE International Symposium on Mixed and Augmented Reality (ISMAR-2012), 11th, November 5-8, Atlanta, Georgia, USA, IEEE Computer Society Press, 2012.
Zusammenfassung
We present a robust real-time capable and simple framework for
segmenting video sequences and live-streams of manual workflows
into the comprising single tasks. Using classifiers trained on these
segments we can follow a user that is performing the workflow in
real-time as well as learn task variants from additional video examples.
Our proposed method neither requires object detection nor
high-level features. Instead we propose a novel measure derived
from image distance that evaluates image properties jointly without
prior segmentation. Our method can cope with repetitive and
free-hand activities and the results are in many cases comparable
or equal to manual task segmentation. One important application
of our method is the automatic creation of a step-by-step task documentation
from a video demonstration. The entire process to automatically
create a fully functional augmented reality manual will
be explained in detail and results are shown.