1.introduction 2.article [1] real time motion capture using a single tof camera (2010) 3.article [2]...
TRANSCRIPT
![Page 1: 1.Introduction 2.Article [1] Real Time Motion Capture Using a Single TOF Camera (2010) 3.Article [2] Real Time Human Pose Recognition In Parts Using a](https://reader036.vdocuments.us/reader036/viewer/2022062421/56649cf85503460f949c90a4/html5/thumbnails/1.jpg)
Human Pose Recognition
![Page 2: 1.Introduction 2.Article [1] Real Time Motion Capture Using a Single TOF Camera (2010) 3.Article [2] Real Time Human Pose Recognition In Parts Using a](https://reader036.vdocuments.us/reader036/viewer/2022062421/56649cf85503460f949c90a4/html5/thumbnails/2.jpg)
Contents
1. Introduction
2. Article [1]
Real Time Motion Capture Using a
Single TOF Camera (2010)
3. Article [2]
Real Time Human Pose Recognition In
Parts Using a Single Depth
Images(2011)
![Page 3: 1.Introduction 2.Article [1] Real Time Motion Capture Using a Single TOF Camera (2010) 3.Article [2] Real Time Human Pose Recognition In Parts Using a](https://reader036.vdocuments.us/reader036/viewer/2022062421/56649cf85503460f949c90a4/html5/thumbnails/3.jpg)
1.1 What Is Pose Recognition?
Fig From [2]
Input Image
armtorso
head
![Page 4: 1.Introduction 2.Article [1] Real Time Motion Capture Using a Single TOF Camera (2010) 3.Article [2] Real Time Human Pose Recognition In Parts Using a](https://reader036.vdocuments.us/reader036/viewer/2022062421/56649cf85503460f949c90a4/html5/thumbnails/4.jpg)
1.2 Motivation
Why do we need this?
Robotics
Smart surveillance
virtual reality
motion analysis
Gaming - Kinect
![Page 5: 1.Introduction 2.Article [1] Real Time Motion Capture Using a Single TOF Camera (2010) 3.Article [2] Real Time Human Pose Recognition In Parts Using a](https://reader036.vdocuments.us/reader036/viewer/2022062421/56649cf85503460f949c90a4/html5/thumbnails/5.jpg)
Kinect – Project Natal
Microsoft Xbox 360 console
“You are the controller”
Launched - 04/11/10
In the first 60 days on the market sold
over 8M units! (Guinness world record)
http://www.youtube.com/watch?v=p2qlHo
xPioM
![Page 6: 1.Introduction 2.Article [1] Real Time Motion Capture Using a Single TOF Camera (2010) 3.Article [2] Real Time Human Pose Recognition In Parts Using a](https://reader036.vdocuments.us/reader036/viewer/2022062421/56649cf85503460f949c90a4/html5/thumbnails/6.jpg)
1.3 Challenges
Real Time???
Full Solution??
Cheap???
OCCLUSIONS???Light?
Shadows?
Clothes?
What is the problem???
![Page 7: 1.Introduction 2.Article [1] Real Time Motion Capture Using a Single TOF Camera (2010) 3.Article [2] Real Time Human Pose Recognition In Parts Using a](https://reader036.vdocuments.us/reader036/viewer/2022062421/56649cf85503460f949c90a4/html5/thumbnails/7.jpg)
1.4 Previous Technology
mocap using markers –
expensive
Multi View camera systems –
limited applicability.
Monocular –
simplified problems.
![Page 8: 1.Introduction 2.Article [1] Real Time Motion Capture Using a Single TOF Camera (2010) 3.Article [2] Real Time Human Pose Recognition In Parts Using a](https://reader036.vdocuments.us/reader036/viewer/2022062421/56649cf85503460f949c90a4/html5/thumbnails/8.jpg)
1.4 New TechnologyTime Of Flight Camera. (TOF)
Dense depth
High frame rate (100 Hz)
Robust to:
Lighting
shadows
other problems.
![Page 9: 1.Introduction 2.Article [1] Real Time Motion Capture Using a Single TOF Camera (2010) 3.Article [2] Real Time Human Pose Recognition In Parts Using a](https://reader036.vdocuments.us/reader036/viewer/2022062421/56649cf85503460f949c90a4/html5/thumbnails/9.jpg)
2. Article [1]Real Time Motion
Capture Using a Single Time Of Flight Camera
(V. Ganapathi et al. CVPR 2010)
![Page 10: 1.Introduction 2.Article [1] Real Time Motion Capture Using a Single TOF Camera (2010) 3.Article [2] Real Time Human Pose Recognition In Parts Using a](https://reader036.vdocuments.us/reader036/viewer/2022062421/56649cf85503460f949c90a4/html5/thumbnails/10.jpg)
Article Contents
2.1 previous work
2.2 What’s new?
2.3 Overview
2.4 results
2.5 limitations & future work
2.6 Evaluation
![Page 11: 1.Introduction 2.Article [1] Real Time Motion Capture Using a Single TOF Camera (2010) 3.Article [2] Real Time Human Pose Recognition In Parts Using a](https://reader036.vdocuments.us/reader036/viewer/2022062421/56649cf85503460f949c90a4/html5/thumbnails/11.jpg)
2.1 Previous workMany many many articles…
(Moeslund et al 2006–covered 350
articles…)
(2006) (2006) (1998)
![Page 12: 1.Introduction 2.Article [1] Real Time Motion Capture Using a Single TOF Camera (2010) 3.Article [2] Real Time Human Pose Recognition In Parts Using a](https://reader036.vdocuments.us/reader036/viewer/2022062421/56649cf85503460f949c90a4/html5/thumbnails/12.jpg)
2.2 What’s new?TOF technology
Propagating information up the kinematic
chain.
Probabilistic model using the unscented
transform.
Multiple GPUs.
![Page 13: 1.Introduction 2.Article [1] Real Time Motion Capture Using a Single TOF Camera (2010) 3.Article [2] Real Time Human Pose Recognition In Parts Using a](https://reader036.vdocuments.us/reader036/viewer/2022062421/56649cf85503460f949c90a4/html5/thumbnails/13.jpg)
2.3 Overview
1. Probabilistic Model
2. Algorithm Overview:
Model Based Hill Climbing Search
Evidence Propagation
Full Algorithm
![Page 14: 1.Introduction 2.Article [1] Real Time Motion Capture Using a Single TOF Camera (2010) 3.Article [2] Real Time Human Pose Recognition In Parts Using a](https://reader036.vdocuments.us/reader036/viewer/2022062421/56649cf85503460f949c90a4/html5/thumbnails/14.jpg)
1 .Probabilistic Model 1 .Probabilistic Model
15 body parts
DAG – Directed Acyclic Graph
1{ }i Nt t iX X pose
tVspeed tzrange scan
DBN– Dynamic Bayesian Network
![Page 15: 1.Introduction 2.Article [1] Real Time Motion Capture Using a Single TOF Camera (2010) 3.Article [2] Real Time Human Pose Recognition In Parts Using a](https://reader036.vdocuments.us/reader036/viewer/2022062421/56649cf85503460f949c90a4/html5/thumbnails/15.jpg)
dynamic Bayesian network (DBN)
Assumptions
Use ray casting to evaluate
distance from measurement.
Goal: Find the most likely states, given previous frame
MAP, i.e.:
Fig From [1]
1( ) 1i i it t tP X V X 1 1| ~ ( , )t t tV V N V
, 1 1ˆ ˆ ˆ ˆ, argmax log ( | , ) log ( , | , )
t tt t X V t t t t t t tX V P z X V P X V X V
1 .Probabilistic Model
kz
![Page 16: 1.Introduction 2.Article [1] Real Time Motion Capture Using a Single TOF Camera (2010) 3.Article [2] Real Time Human Pose Recognition In Parts Using a](https://reader036.vdocuments.us/reader036/viewer/2022062421/56649cf85503460f949c90a4/html5/thumbnails/16.jpg)
2 .Algorithm Overview
1. Hill climbing search (HC)
2. Evidence Propagation –EP
![Page 17: 1.Introduction 2.Article [1] Real Time Motion Capture Using a Single TOF Camera (2010) 3.Article [2] Real Time Human Pose Recognition In Parts Using a](https://reader036.vdocuments.us/reader036/viewer/2022062421/56649cf85503460f949c90a4/html5/thumbnails/17.jpg)
2.1 Hill Climbing Search (HC)
Fig From [1]
0.05m
1ˆ,t t tX fromV X
0.05m
Calculate
evaluate likelihood choose best point!
1( | )i it tP V V Grid around
itVSamplei
Coarse to fine Grids.
![Page 18: 1.Introduction 2.Article [1] Real Time Motion Capture Using a Single TOF Camera (2010) 3.Article [2] Real Time Human Pose Recognition In Parts Using a](https://reader036.vdocuments.us/reader036/viewer/2022062421/56649cf85503460f949c90a4/html5/thumbnails/18.jpg)
2.1 Hill Climbing Search (HC)
The good:
Simple
Fast
run in parallel in GPUS
The Bad:
Local optimum
Ridges, Plateau, Alleys
Can lose track when motion is fast ,or occlusions
occur.
![Page 19: 1.Introduction 2.Article [1] Real Time Motion Capture Using a Single TOF Camera (2010) 3.Article [2] Real Time Human Pose Recognition In Parts Using a](https://reader036.vdocuments.us/reader036/viewer/2022062421/56649cf85503460f949c90a4/html5/thumbnails/19.jpg)
2.2 Evidence Propagation
Also has 3 stages:
1. Body part detection (C. Plagemann et al 2010)
2. Probabilistic Inverse Kinematics
3. Data association and inference
![Page 20: 1.Introduction 2.Article [1] Real Time Motion Capture Using a Single TOF Camera (2010) 3.Article [2] Real Time Human Pose Recognition In Parts Using a](https://reader036.vdocuments.us/reader036/viewer/2022062421/56649cf85503460f949c90a4/html5/thumbnails/20.jpg)
2.2.1 Body Part Detection
Bottom up approach:
1. Locate interest points with AGEX –
Accumulative Geodesic Extrema.
2. Find orientation.
3. Classify the head, foots and hands using local shape
descriptors.
Fig From [3]
![Page 21: 1.Introduction 2.Article [1] Real Time Motion Capture Using a Single TOF Camera (2010) 3.Article [2] Real Time Human Pose Recognition In Parts Using a](https://reader036.vdocuments.us/reader036/viewer/2022062421/56649cf85503460f949c90a4/html5/thumbnails/21.jpg)
2.2.1 Body Part Detection
Results:
Fig From [3]
![Page 22: 1.Introduction 2.Article [1] Real Time Motion Capture Using a Single TOF Camera (2010) 3.Article [2] Real Time Human Pose Recognition In Parts Using a](https://reader036.vdocuments.us/reader036/viewer/2022062421/56649cf85503460f949c90a4/html5/thumbnails/22.jpg)
2.2.2 Probabalistic inverse kinematics (EP)
51{ , , }i ip Head Hands Legs of X ˆ ( 1,..., )jp j N
?
Assume Correspondence
Need new MAP conditioned on .
Problem – isn’t linear!
Solution: Linearize with the unscented Kalman filter .
Easy to determine .
1 1ˆ ˆ( , , )i t t tp V X V
1 1ˆ ˆ ˆ( | , , )t t t jP V V X p
1ˆ ˆ,t jX p
ˆi jp p
![Page 23: 1.Introduction 2.Article [1] Real Time Motion Capture Using a Single TOF Camera (2010) 3.Article [2] Real Time Human Pose Recognition In Parts Using a](https://reader036.vdocuments.us/reader036/viewer/2022062421/56649cf85503460f949c90a4/html5/thumbnails/23.jpg)
2.3 Full Algorithm
HC
Part Detection
Remove ExplainedSuggestions.Coresspond: by body parts
ˆ{( , )}i jp p
X’
HC
PreviousMAP
DepthImage
X’>Xbest?
X’
Xbest
EP
![Page 24: 1.Introduction 2.Article [1] Real Time Motion Capture Using a Single TOF Camera (2010) 3.Article [2] Real Time Human Pose Recognition In Parts Using a](https://reader036.vdocuments.us/reader036/viewer/2022062421/56649cf85503460f949c90a4/html5/thumbnails/24.jpg)
2.4 Results Experiments:
28 real depth image sequences.
Ground Truth - tracking markers.
, – real marker position
– estimated position
perfect tracks.
fault tracking.
Compared 3 algorithms: EP, HC, HC+EP .
1
ˆ|| ||Mi i
avgi
m m
M
im
ˆ im
0.1avg m
0.3avg m
![Page 25: 1.Introduction 2.Article [1] Real Time Motion Capture Using a Single TOF Camera (2010) 3.Article [2] Real Time Human Pose Recognition In Parts Using a](https://reader036.vdocuments.us/reader036/viewer/2022062421/56649cf85503460f949c90a4/html5/thumbnails/25.jpg)
2.4 Results
best – HC+EP, worse – EP.
Runs close to real time.
HC: 6 frames per second.
HC+EP: 4-6 frames per second.Fig From [1]
BiggerDifference
Harder
![Page 26: 1.Introduction 2.Article [1] Real Time Motion Capture Using a Single TOF Camera (2010) 3.Article [2] Real Time Human Pose Recognition In Parts Using a](https://reader036.vdocuments.us/reader036/viewer/2022062421/56649cf85503460f949c90a4/html5/thumbnails/26.jpg)
2.4 Results
HC
HC+EP
Lose trackExtreme case – 27:
Fig From [1]
![Page 27: 1.Introduction 2.Article [1] Real Time Motion Capture Using a Single TOF Camera (2010) 3.Article [2] Real Time Human Pose Recognition In Parts Using a](https://reader036.vdocuments.us/reader036/viewer/2022062421/56649cf85503460f949c90a4/html5/thumbnails/27.jpg)
2.5 Limitations & Future workLimitations:
Manual Initialization.
Tracking more than one person at a time.
Using temporal data – consume more time,
reinitialization problem.
Future work:
improving the speed.
combining with color cameras
fully automatic model initialization.
Track more than 1 person.
![Page 28: 1.Introduction 2.Article [1] Real Time Motion Capture Using a Single TOF Camera (2010) 3.Article [2] Real Time Human Pose Recognition In Parts Using a](https://reader036.vdocuments.us/reader036/viewer/2022062421/56649cf85503460f949c90a4/html5/thumbnails/28.jpg)
2.6 Evaluation Well Written
Self Contained
Novel combination of existing parts
New technology
Achieving goals (real time)
Missing examples on probabilistic model.
Not clear how is defined
Extensively validated:
Data set and code available
not enough visual examples in article
No comparison to different
algorithms
0X
![Page 29: 1.Introduction 2.Article [1] Real Time Motion Capture Using a Single TOF Camera (2010) 3.Article [2] Real Time Human Pose Recognition In Parts Using a](https://reader036.vdocuments.us/reader036/viewer/2022062421/56649cf85503460f949c90a4/html5/thumbnails/29.jpg)
3. Article [2]Real Time Human Pose
Recognition In Parts From Single Depth
Images (Shotton et al. & Xbox incubation
Microsoft Research 2011)
![Page 30: 1.Introduction 2.Article [1] Real Time Motion Capture Using a Single TOF Camera (2010) 3.Article [2] Real Time Human Pose Recognition In Parts Using a](https://reader036.vdocuments.us/reader036/viewer/2022062421/56649cf85503460f949c90a4/html5/thumbnails/30.jpg)
Article Contents
2.1 previous work
2.2 What’s new?
2.3 Overview
2.4 results
2.5 limitations & future work
2.6 Evaluation
![Page 31: 1.Introduction 2.Article [1] Real Time Motion Capture Using a Single TOF Camera (2010) 3.Article [2] Real Time Human Pose Recognition In Parts Using a](https://reader036.vdocuments.us/reader036/viewer/2022062421/56649cf85503460f949c90a4/html5/thumbnails/31.jpg)
2.1 Previous work Same as Article [1].
![Page 32: 1.Introduction 2.Article [1] Real Time Motion Capture Using a Single TOF Camera (2010) 3.Article [2] Real Time Human Pose Recognition In Parts Using a](https://reader036.vdocuments.us/reader036/viewer/2022062421/56649cf85503460f949c90a4/html5/thumbnails/32.jpg)
2.2 What’s new? Using no temporal information – robust
and
fast (200 frames per second).
Object recognition approach.
per pixel classification.
Large and highly varied
training dataset .
Fig From [2]
![Page 33: 1.Introduction 2.Article [1] Real Time Motion Capture Using a Single TOF Camera (2010) 3.Article [2] Real Time Human Pose Recognition In Parts Using a](https://reader036.vdocuments.us/reader036/viewer/2022062421/56649cf85503460f949c90a4/html5/thumbnails/33.jpg)
2.3 Overview
1. Database construction
2. Body part inference and joint proposals:
Goals:
computational efficiency and robustness
![Page 34: 1.Introduction 2.Article [1] Real Time Motion Capture Using a Single TOF Camera (2010) 3.Article [2] Real Time Human Pose Recognition In Parts Using a](https://reader036.vdocuments.us/reader036/viewer/2022062421/56649cf85503460f949c90a4/html5/thumbnails/34.jpg)
1 .Database
Pose estimation is often overcome lack of training
data… why???
Huge color and texture variability.
Computer simulation don’t produce the range of
volitional motions of a human subject.
![Page 35: 1.Introduction 2.Article [1] Real Time Motion Capture Using a Single TOF Camera (2010) 3.Article [2] Real Time Human Pose Recognition In Parts Using a](https://reader036.vdocuments.us/reader036/viewer/2022062421/56649cf85503460f949c90a4/html5/thumbnails/35.jpg)
2 .Data base
Fig From [2]
100k mocap frames Synthetic rendering pipeline
![Page 36: 1.Introduction 2.Article [1] Real Time Motion Capture Using a Single TOF Camera (2010) 3.Article [2] Real Time Human Pose Recognition In Parts Using a](https://reader036.vdocuments.us/reader036/viewer/2022062421/56649cf85503460f949c90a4/html5/thumbnails/36.jpg)
1 .Database
Real data
Synthetic data
Which is real???
Fig From [2]
![Page 37: 1.Introduction 2.Article [1] Real Time Motion Capture Using a Single TOF Camera (2010) 3.Article [2] Real Time Human Pose Recognition In Parts Using a](https://reader036.vdocuments.us/reader036/viewer/2022062421/56649cf85503460f949c90a4/html5/thumbnails/37.jpg)
2 .Body part inference
1. Body part labeling
2. Depth image features
3. Randomized decision forests
4. Joint position proposals
![Page 38: 1.Introduction 2.Article [1] Real Time Motion Capture Using a Single TOF Camera (2010) 3.Article [2] Real Time Human Pose Recognition In Parts Using a](https://reader036.vdocuments.us/reader036/viewer/2022062421/56649cf85503460f949c90a4/html5/thumbnails/38.jpg)
2.1 Body part labeling
31 body parts labeled .
The problem now can be solved by an efficient
classification algorithms.
Fig From [2]
Head Up RightHead Up Left
![Page 39: 1.Introduction 2.Article [1] Real Time Motion Capture Using a Single TOF Camera (2010) 3.Article [2] Real Time Human Pose Recognition In Parts Using a](https://reader036.vdocuments.us/reader036/viewer/2022062421/56649cf85503460f949c90a4/html5/thumbnails/39.jpg)
2.2 Depth comparison features
Simple depth comparison features:(1)
– depth at pixel x in image I, offset
normalization - depth invariant.
computational efficiency:
no preprocessing.
( )Id x ( , )u v
Fig From [2]
![Page 40: 1.Introduction 2.Article [1] Real Time Motion Capture Using a Single TOF Camera (2010) 3.Article [2] Real Time Human Pose Recognition In Parts Using a](https://reader036.vdocuments.us/reader036/viewer/2022062421/56649cf85503460f949c90a4/html5/thumbnails/40.jpg)
2.3 Randomized Decision forests
How does it work?
Node = feature
Classify pixel x:
f and a threshold
1
1( | , ) ( | , )
T
tt
P c I x P c I xT
Fig From [2]
Pixel x
![Page 41: 1.Introduction 2.Article [1] Real Time Motion Capture Using a Single TOF Camera (2010) 3.Article [2] Real Time Human Pose Recognition In Parts Using a](https://reader036.vdocuments.us/reader036/viewer/2022062421/56649cf85503460f949c90a4/html5/thumbnails/41.jpg)
2.3 Randomized Decision forests
Training Algorithm: 1M Images – 2000 pixels
Per image
( , )
( , )
| ( ) |argmax ( ) ( ) ( ) ( ( ))
| |s
ss l r
QG G H Q H Q
Q
*H-antropy
Training 3 trees, depth 20, 1M images~ 1 day (1000 core
cluster)
1M images*2000pixels*2000 *50 =
f 142 10 ...computations
![Page 42: 1.Introduction 2.Article [1] Real Time Motion Capture Using a Single TOF Camera (2010) 3.Article [2] Real Time Human Pose Recognition In Parts Using a](https://reader036.vdocuments.us/reader036/viewer/2022062421/56649cf85503460f949c90a4/html5/thumbnails/42.jpg)
2.3 Randomized Decision forests
Fig From [2]
Trained tree:
![Page 43: 1.Introduction 2.Article [1] Real Time Motion Capture Using a Single TOF Camera (2010) 3.Article [2] Real Time Human Pose Recognition In Parts Using a](https://reader036.vdocuments.us/reader036/viewer/2022062421/56649cf85503460f949c90a4/html5/thumbnails/43.jpg)
2.4 Joint Position Proposal
Local mode finding approach based on mean shift with a
weighted Gaussian kernel.
Density estimator:
2
1
ˆ( ) expN
ic ic
i c
x xf x w
b
2( | , ) ( )ic i I iw P c I x d x
Fig From [4]
outliersCenter of mass
![Page 44: 1.Introduction 2.Article [1] Real Time Motion Capture Using a Single TOF Camera (2010) 3.Article [2] Real Time Human Pose Recognition In Parts Using a](https://reader036.vdocuments.us/reader036/viewer/2022062421/56649cf85503460f949c90a4/html5/thumbnails/44.jpg)
2.4 Results Experiments:
8800 frames of real depth images.
5000 synthetic depth images.
Also evaluate Article [1] dataset.
Measures :
1. Classification accuracy – confusion
matrix.
2. joint accuracy –mean Average Precision
(mAP)
results within D=0.1m –TP.
![Page 45: 1.Introduction 2.Article [1] Real Time Motion Capture Using a Single TOF Camera (2010) 3.Article [2] Real Time Human Pose Recognition In Parts Using a](https://reader036.vdocuments.us/reader036/viewer/2022062421/56649cf85503460f949c90a4/html5/thumbnails/45.jpg)
Fault
Fig From [2]
![Page 46: 1.Introduction 2.Article [1] Real Time Motion Capture Using a Single TOF Camera (2010) 3.Article [2] Real Time Human Pose Recognition In Parts Using a](https://reader036.vdocuments.us/reader036/viewer/2022062421/56649cf85503460f949c90a4/html5/thumbnails/46.jpg)
2.4 Results- Classification accuracy high correlation between real and synthetic.
Depth of tree – most effective
Fig From [2]
![Page 47: 1.Introduction 2.Article [1] Real Time Motion Capture Using a Single TOF Camera (2010) 3.Article [2] Real Time Human Pose Recognition In Parts Using a](https://reader036.vdocuments.us/reader036/viewer/2022062421/56649cf85503460f949c90a4/html5/thumbnails/47.jpg)
2.4 Results - Joint Prediction Comparing the algorithm on:
real set (red) – mAP 0.731
ground truth set (blue) – mAP 0.914
mAP 0.984 – upper
body
Fig From [2]
![Page 48: 1.Introduction 2.Article [1] Real Time Motion Capture Using a Single TOF Camera (2010) 3.Article [2] Real Time Human Pose Recognition In Parts Using a](https://reader036.vdocuments.us/reader036/viewer/2022062421/56649cf85503460f949c90a4/html5/thumbnails/48.jpg)
2.4 Results- Joint PredictionComparing algorithm to ideal Nearest Neighbor
matching, and realistic NN - Chamfer NN.
Fig From [2]
![Page 49: 1.Introduction 2.Article [1] Real Time Motion Capture Using a Single TOF Camera (2010) 3.Article [2] Real Time Human Pose Recognition In Parts Using a](https://reader036.vdocuments.us/reader036/viewer/2022062421/56649cf85503460f949c90a4/html5/thumbnails/49.jpg)
2.4 Results- Joint PredictionComparison to Article[1]:
Run on the same dataset
Better results (even without temporal
data)
Runs 10x faster.
Fig From [2]
![Page 50: 1.Introduction 2.Article [1] Real Time Motion Capture Using a Single TOF Camera (2010) 3.Article [2] Real Time Human Pose Recognition In Parts Using a](https://reader036.vdocuments.us/reader036/viewer/2022062421/56649cf85503460f949c90a4/html5/thumbnails/50.jpg)
2.4 Results- Joint PredictionFull rotations and multiple people
Right-left ambiguity
mAP of 0.655 ( good for our uses)
Result VideoFig From [2]
![Page 51: 1.Introduction 2.Article [1] Real Time Motion Capture Using a Single TOF Camera (2010) 3.Article [2] Real Time Human Pose Recognition In Parts Using a](https://reader036.vdocuments.us/reader036/viewer/2022062421/56649cf85503460f949c90a4/html5/thumbnails/51.jpg)
2.5 Limitations & Future workFuture work:
better synthesis pipeline
Is there efficient approach that directly
regress joint positions? (already done in
future
work -
Efficient offset regression of body joint position
s
)
![Page 52: 1.Introduction 2.Article [1] Real Time Motion Capture Using a Single TOF Camera (2010) 3.Article [2] Real Time Human Pose Recognition In Parts Using a](https://reader036.vdocuments.us/reader036/viewer/2022062421/56649cf85503460f949c90a4/html5/thumbnails/52.jpg)
2.6 Evaluation Well Written
Self Contained
Novel combination of existing parts
New technology
Achieving goals (real time)
Extensively validated:
Used in real console
Many results graphs and examples
(Another pdf of supplementary
material)
Broad comparison to other
algorithms
data set and code not available
![Page 53: 1.Introduction 2.Article [1] Real Time Motion Capture Using a Single TOF Camera (2010) 3.Article [2] Real Time Human Pose Recognition In Parts Using a](https://reader036.vdocuments.us/reader036/viewer/2022062421/56649cf85503460f949c90a4/html5/thumbnails/53.jpg)
References[1] Real Time Motion Capture Using a Single TOF Camera (V.
Ganapathi et al. 2010)
[2] Real Time Human Pose Recognition In Parts Using a Single
Depth Images(Shotton et al. & Xbox Incubation 2011)
[3] Real time identification and localization of body parts from
depth images (C. Plagemann et al. 2010)
[4] Computer Graphics course (046746), Technion.
![Page 54: 1.Introduction 2.Article [1] Real Time Motion Capture Using a Single TOF Camera (2010) 3.Article [2] Real Time Human Pose Recognition In Parts Using a](https://reader036.vdocuments.us/reader036/viewer/2022062421/56649cf85503460f949c90a4/html5/thumbnails/54.jpg)
Questions?