UC Berkeley

1
Recognizing Action at a Distance

• A.A. Efros, A.C. Berg, G. Mori, J. Malik
• UC Berkeley

2
Looking at People
Far field
Near field

• 3-pixel man
• Blob tracking
• vast surveillance literature

• 300-pixel man
• Limb tracking
• e.g. Yacoob Black, Rao Shah, etc.

3
Medium-field Recognition
4
Appearance vs. Motion
5
Goals

• Recognize human actions at a distance
• Low resolution, noisy data
• Moving camera, occlusions
• Wide range of actions (including non-periodic)

6
Our Approach

• Motion-based approach
• Non-parametric use large amount of data
• Classify a novel motion by finding the most
  similar motion from the training set
• Related Work
• Periodicity analysis
• Polana Nelson Seitz Dyer Bobick et al
  Cutler Davis Collins et al.
• Model-free
• Temporal Templates Bobick Davis
• Orientation histograms Freeman et al Zelnik
  Irani
• Using MoCap data Zhao Nevatia, Ramanan
  Forsyth

7
Gathering action data

• Tracking
• Simple correlation-based tracker
• User-initialized

8
Figure-centric Representation

• Stabilized spatio-temporal volume
• No translation information
• All motion caused by persons limbs
• Good news indifferent to camera motion
• Bad news hard!
• Good test to see if actions, not just
  translation, are being captured

9
Remembrance of Things Past

• Explain novel motion sequence by matching to
  previously seen video clips
• For each frame, match based on some temporal
  extent

input sequence
Challenge how to compare motions?
10
How to describe motion?

• Appearance
• Not preserved across different clothing
• Gradients (spatial, temporal)
• same (e.g. contrast reversal)
• Edges/Silhouettes
• Too unreliable
• Optical flow
• Explicitly encodes motion
• Least affected by appearance
• but too noisy

11
Spatial Motion Descriptor
Image frame
Optical flow
12
Spatio-temporal Motion Descriptor


Sequence A
S


Sequence B
t
13
Football Actions matching
Input Sequence
Matched Frames
input
matched
14
Football Actions classification
10 actions 4500 total frames 13-frame motion
descriptor
15
Classifying Ballet Actions
16 Actions 24800 total frames 51-frame motion
descriptor. Men used to classify women and vice
versa.
16
Classifying Tennis Actions
6 actions 4600 frames 7-frame motion
descriptor Woman player used as training, man as
testing.
17
Classifying Tennis

• Red bars show classification results

18
Querying the Database
input sequence
database
19
2D Skeleton Transfer

• We annotate database with 2D joint positions
• After matching, transfer data to novel sequence
• Ajust the match for best fit

Input sequence
Transferred 2D skeletons
20
3D Skeleton Transfer

• We populate database with rendered stick figures
  from 3D Motion Capture data
• Matching as before, we get 3D joint positions
  (kind of)!

Input sequence
Transferred 3D skeletons
21
Do as I Do Motion Synthesis
input sequence
synthetic sequence

• Matching two things
• Motion similarity across sequences
• Appearance similarity within sequence (like
  VideoTextures)
• Dynamic Programming

22
Do as I Do
Source Motion
Source Appearance
3400 Frames
Result
23
Do as I Say Synthesis
run walk left swing walk
right jog
run
jog
swing
walk right
walk left
synthetic sequence

• Synthesize given action labels
• e.g. video game control

24
Do as I Say

• Red box shows when constraint is applied

25
Actor Replacement
SHOW VIDEO (GregWorldCup.avi, DivX)
26
Conclusions

• In medium field action is about motion
• What we propose
• A way of matching motions at coarse scale
• What we get out
• Action recognition
• Skeleton transfer
• Synthesis Do as I Do Do as I say
• What we learned?
• A lot to be said for the little guy!