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Documenting Motion Sequences with Personalized Annotation System
Kanav Kahol,Priyamvada Tripathi, and
Sethuraman PanchanathanArizona State University
Yu-song Syu
20060822
IEEE Multimedia 2006
Outline Gestures & complex motion sequences Steps of annotation
Modeling gestures anatomically Motion capture Gesture segmentation Gesture recognition Movement annotation
Results Future work
Gestures A sequence of poses
Modeled by state transition Each state corresponds to a pose in the
sequence
Start pose
Endpose
When it becomes complex…
In dance, a large vocabulary of gestures are used
A scalable gesture segmentation / recognition methodology is needed
HMM is needed here
HMM – Hidden Markov Model
We have: Possible symbols Possible states Possibility of transition between
states Possibility of symbols in every state
Symbol series are given State series are hidden
Modeling gestures anatomically
Model the anatomy with 23 segments & 14 joints A parent segment inherits the characteristics of
its children
Two adjacent segments can be perceived as one when They have similar motion vectors Angle of the joint between them
doesn’t change in a time period Dynamic body hierarchy
Modeling gestures anatomically
Dynamic body hierarchy
Segments behaving as one
unit have the same color
Motion capsure
7 choreographers Each creates 3 short dance sequences Every sequences are repeated 3 times Choreographers write down:
Original score for every dance sequence Detail score for every gesture Score: a verbal description
Motion capture
Gesture segmentation
For every body segment Derivate the spatial orientation, velocity, and
acceleration Dynamic hierarchy
Compute the activity SegmentForce = SegmentMass * SegmentAcceleration SegmentMomentum = SegmentMass * SegmentVelocity SegmentKE = SegmentMass * segmentVelocity2
Derive parent activities by vector addition of roots Gesture boundary determination
Gesture segmentation Gesture boundary determination
Find out local minima as binary triples When force reaches its minimum, mark “1” In common with momentum and KE I.e. (100), (011), …
Not every local minimum is a gesture boundary 22 real-world physical configurations in which
adjacent body segments could coalesce We use the 23 triples and 22-elements vector
to train classifier to figure out whether the local minimum is a gesture boundary
Gesture recognition
Find minima of total force of segments Find stabilization of joints
Change of joint angle doesn’t exceed a threshold during a time period
segmentHMM with 23 states jointHMM with 14 states cHMM couples above-mentioned
HMMs
cHMM
cHMM
Θc’c: coupling weight from jointHMM to segmentHMM
d(t,i): distance between segmentt and jointi
Movement annotation
Movement annotation can be useful while teaching dance
Use Anvil annotation software while training http://www.dfki.de/~kipp/anvil Choreographers can use it to
add/modify annotations and set gesture boundaries
Anvil
Motion annotation results The proposed system is simple to use
Xml language and Anvil interface A manual annotation of a 4-5 minute dance
takes about 60 minutes This system takes only 1 minute
A 6-9 percent improvement in accuracy
Future work
Extend this system to annotate generic human movements i.e. walking, running, and washing
utensils Develop a common motion
language with this kind of software