Recognizing Human-Object Interactions in Still Images by Modeling the Mutual Context of Objects and Human Poses
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The document discusses a methodology for recognizing human-object interactions in still images by analyzing the mutual context of objects and human poses. It outlines challenges in human pose estimation and object detection, emphasizing the importance of contextual cues to improve accuracy in recognizing activities such as sports. The approach includes model representation, learning, inference, and presents experimental results demonstrating its effectiveness over previous methods.
![19
H
A
Mutual Context Model Representation
• More than one H for each A;
• Unobserved during training.
A:
Croquet
shot
Volleyball
smash
Tennis
forehand
Intra-class variations
Activity
Object
Human pose
Body parts
lP: location; θP: orientation; sP: scale.
Croquet
mallet
Volleyball
Tennis
racket
O:
H:
P:
f: Shape context. [Belongie et al, 2002]
P1
Image evidence
fO
f1 f2 fN
O
P2 PN](https://image.slidesharecdn.com/hci-130520131258-phpapp02/85/Recognizing-Human-Object-Interactions-in-Still-Images-by-Modeling-the-Mutual-Context-of-Objects-and-Human-Poses-19-320.jpg)
![23
H
O
A
fO
f1 f2 fN
P1 P2 PN
Mutual Context Model Representation
• and : Discriminative
part detection scores.
( , )e OO f ( , )ne n PP f
[Andriluka et al, 2009]
Shape context + AdaBoost
• Learn structural connectivity among
the body parts and the object.
[Belongie et al, 2002]
[Viola & Jones, 2001]
( , )e OO f
( , )ne n PP f
( , )e A O ( , )e A H ( , )e O H• , , : Frequency
of co-occurrence between A, O, and H.
• , , : Spatial
relationship among object and body parts.
( , )e nO P ( , )e m nP P( , )e nH P
bin binn n nO P O P O Pl l s s
location orientation size
e e
e E
w
Markov Random Field
Clique
potential
Clique
weight](https://image.slidesharecdn.com/hci-130520131258-phpapp02/85/Recognizing-Human-Object-Interactions-in-Still-Images-by-Modeling-the-Mutual-Context-of-Objects-and-Human-Poses-23-320.jpg)
![I
37
Model Inference
The learned models
Head detection
Torso detection
Tennis racket detection
Layout of the object and body parts.
Compositional
Inference
[Chen et al, 2007]
* *
1 1 1 1,, , , n n
A H O P](https://image.slidesharecdn.com/hci-130520131258-phpapp02/85/Recognizing-Human-Object-Interactions-in-Still-Images-by-Modeling-the-Mutual-Context-of-Objects-and-Human-Poses-37-320.jpg)
![40
Dataset and Experiment Setup
• Object detection;
• Pose estimation;
• Activity classification.
Tasks:
[Gupta et al, 2009]
Cricket
defensive shot
Cricket
bowling
Croquet
shot
Tennis
forehand
Tennis
serve
Volleyball
smash
Sport data set: 6 classes
180 training (supervised with object and part locations) & 120 testing images](https://image.slidesharecdn.com/hci-130520131258-phpapp02/85/Recognizing-Human-Object-Interactions-in-Still-Images-by-Modeling-the-Mutual-Context-of-Objects-and-Human-Poses-40-320.jpg)
![[Gupta et al, 2009]
Cricket
defensive shot
Cricket
bowling
Croquet
shot
Tennis
forehand
Tennis
serve
Volleyball
smash
Sport data set: 6 classes
41
Dataset and Experiment Setup
• Object detection;
• Pose estimation;
• Activity classification.
Tasks:
180 training (supervised with object and part locations) & 120 testing images](https://image.slidesharecdn.com/hci-130520131258-phpapp02/85/Recognizing-Human-Object-Interactions-in-Still-Images-by-Modeling-the-Mutual-Context-of-Objects-and-Human-Poses-41-320.jpg)
![0 0.2 0.4 0.6 0.8 1
0
0.2
0.4
0.6
0.8
1
Recall
Precision
Object Detection Results
Cricket bat
42
Valid
region
Croquet mallet Tennis racket Volleyball
0 0.2 0.4 0.6 0.8 1
0
0.2
0.4
0.6
0.8
1
Recall
Precision
Cricket ball
Our
Method
Sliding
window
Pedestrian
context
[Andriluka
et al, 2009]
[Dalal &
Triggs, 2006]](https://image.slidesharecdn.com/hci-130520131258-phpapp02/85/Recognizing-Human-Object-Interactions-in-Still-Images-by-Modeling-the-Mutual-Context-of-Objects-and-Human-Poses-42-320.jpg)
![44
Dataset and Experiment Setup
• Object detection;
• Pose estimation;
• Activity classification.
Tasks:
[Gupta et al, 2009]
Cricket
defensive shot
Cricket
bowling
Croquet
shot
Tennis
forehand
Tennis
serve
Volleyball
smash
Sport data set: 6 classes
180 training & 120 testing images](https://image.slidesharecdn.com/hci-130520131258-phpapp02/85/Recognizing-Human-Object-Interactions-in-Still-Images-by-Modeling-the-Mutual-Context-of-Objects-and-Human-Poses-44-320.jpg)
![48
Dataset and Experiment Setup
• Object detection;
• Pose estimation;
• Activity classification.
Tasks:
[Gupta et al, 2009]
Cricket
defensive shot
Cricket
bowling
Croquet
shot
Tennis
forehand
Tennis
serve
Volleyball
smash
Sport data set: 6 classes
180 training & 120 testing images](https://image.slidesharecdn.com/hci-130520131258-phpapp02/85/Recognizing-Human-Object-Interactions-in-Still-Images-by-Modeling-the-Mutual-Context-of-Objects-and-Human-Poses-48-320.jpg)
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Recognizing Human-Object Interactions in Still Images by Modeling the Mutual Context of Objects and Human Poses
- 1. Recognizing Human-Object Interactions in Still Images by Modeling the Mutual Context of Objects and Human Poses Presented By Arwa Chittalwala Irfan Shaikh Heena Patel 1
- 2. Robots interact with objects Automatic sports commentary “Kobe is dunking the ball.” 2 Human-Object Interaction Medical care
- 3. 3 Vs. Human-Object Interaction Playing saxophone Playing bassoon Playing saxophone Grouplet is a generic feature for structured objects, or interactions of groups of objects. (Previous talk: Grouplet) Caltech101 HOI activity: Tennis Forehand Holistic image based classification Detailed understanding and reasoning Berg & Malik, 2005 Grauman & Darrell, 2005 Gehler & Nowozin, 2009 OURS 48% 59% 77% 62%
- 4. 4 Human-Object Interaction Torso Head • Human pose estimation Holistic image based classification Detailed understanding and reasoning
- 5. 5 Human-Object Interaction Tennis racket • Human pose estimation Holistic image based classification Detailed understanding and reasoning • Object detection
- 6. 6 Human-Object Interaction • Human pose estimation Holistic image based classification Detailed understanding and reasoning • Object detection Torso Head Tennis racket HOI activity: Tennis Forehand
- 7. • Background and Intuition • Mutual Context of Object and Human Pose Model Representation Model Learning Model Inference • Experiments • Conclusion Outline 7
- 8. • Background and Intuition • Mutual Context of Object and Human Pose Model Representation Model Learning Model Inference • Experiments • Conclusion Outline 8
- 9. • Felzenszwalb & Huttenlocher, 2005 • Ren et al, 2005 • Ramanan, 2006 • Ferrari et al, 2008 • Yang & Mori, 2008 • Andriluka et al, 2009 • Eichner & Ferrari, 2009 Difficult part appearance Self-occlusion Image region looks like a body part Human pose estimation & Object detection 9 Human pose estimation is challenging.
- 10. Human pose estimation & Object detection 10 Human pose estimation is challenging. • Felzenszwalb & Huttenlocher, 2005 • Ren et al, 2005 • Ramanan, 2006 • Ferrari et al, 2008 • Yang & Mori, 2008 • Andriluka et al, 2009 • Eichner & Ferrari, 2009
- 11. Human pose estimation & Object detection 11 Facilitate Given the object is detected.
- 12. • Viola & Jones, 2001 • Lampert et al, 2008 • Divvala et al, 2009 • Vedaldi et al, 2009 Small, low-resolution, partially occluded Image region similar to detection target Human pose estimation & Object detection 12 Object detection is challenging
- 13. Human pose estimation & Object detection 13 Object detection is challenging • Viola & Jones, 2001 • Lampert et al, 2008 • Divvala et al, 2009 • Vedaldi et al, 2009
- 14. Human pose estimation & Object detection 14 Facilitate Given the pose is estimated.
- 15. Human pose estimation & Object detection 15 Mutual Context
- 16. • Hoiem et al, 2006 • Rabinovich et al, 2007 • Oliva & Torralba, 2007 • Heitz & Koller, 2008 • Desai et al, 2009 • Divvala et al, 2009 • Murphy et al, 2003 • Shotton et al, 2006 • Harzallah et al, 2009 • Li, Socher & Fei-Fei, 2009 • Marszalek et al, 2009 • Bao & Savarese, 2010 Context in Computer Vision ~3-4% with context without context Helpful, but only moderately outperform better Previous work – Use context cues to facilitate object detection: • Viola & Jones, 2001 • Lampert et al, 2008 16
- 17. Context in Computer Vision Our approach – Two challenging tasks serve as mutual context of each other: With mutual context: Without context: 17 ~3-4% with context without context Helpful, but only moderately outperform better Previous work – Use context cues to facilitate object detection: • Hoiem et al, 2006 • Rabinovich et al, 2007 • Oliva & Torralba, 2007 • Heitz & Koller, 2008 • Desai et al, 2009 • Divvala et al, 2009 • Murphy et al, 2003 • Shotton et al, 2006 • Harzallah et al, 2009 • Li, Socher & Fei-Fei, 2009 • Marszalek et al, 2009 • Bao & Savarese, 2010
- 18. • Background and Intuition • Mutual Context of Object and Human Pose Model Representation Model Learning Model Inference • Experiments • Conclusion Outline 18
- 19. 19 H A Mutual Context Model Representation • More than one H for each A; • Unobserved during training. A: Croquet shot Volleyball smash Tennis forehand Intra-class variations Activity Object Human pose Body parts lP: location; θP: orientation; sP: scale. Croquet mallet Volleyball Tennis racket O: H: P: f: Shape context. [Belongie et al, 2002] P1 Image evidence fO f1 f2 fN O P2 PN
- 20. 20 Mutual Context Model Representation ( , )e O H ( , )e A O ( , )e A H e e e E w Markov Random Field Clique potential Clique weight O P1 PN fO H A P2 f1 f2 fN ( , )e A O ( , )e A H ( , )e O H• , , : Frequency of co-occurrence between A, O, and H.
- 21. 21 A f1 f2 fN Mutual Context Model Representation ( , )e nO P ( , )e m nP P fO P1 PNP2 O H• , , : Spatial relationship among object and body parts. ( , )e nO P ( , )e m nP P( , )e nH P bin binn n nO P O P O Pl l s s location orientation size ( , )e nH P e e e E w Markov Random Field Clique potential Clique weight ( , )e A O ( , )e A H ( , )e O H• , , : Frequency of co-occurrence between A, O, and H.
- 22. 22 H A f1 f2 fN Mutual Context Model Representation Obtained by structure learning fO PNP1 P2 O • Learn structural connectivity among the body parts and the object. ( , )e A O ( , )e A H ( , )e O H• , , : Frequency of co-occurrence between A, O, and H. • , , : Spatial relationship among object and body parts. ( , )e nO P ( , )e m nP P( , )e nH P bin binn n nO P O P O Pl l s s location orientation size ( , )e nO P ( , )e m nP P ( , )e nH P e e e E w Markov Random Field Clique potential Clique weight
- 23. 23 H O A fO f1 f2 fN P1 P2 PN Mutual Context Model Representation • and : Discriminative part detection scores. ( , )e OO f ( , )ne n PP f [Andriluka et al, 2009] Shape context + AdaBoost • Learn structural connectivity among the body parts and the object. [Belongie et al, 2002] [Viola & Jones, 2001] ( , )e OO f ( , )ne n PP f ( , )e A O ( , )e A H ( , )e O H• , , : Frequency of co-occurrence between A, O, and H. • , , : Spatial relationship among object and body parts. ( , )e nO P ( , )e m nP P( , )e nH P bin binn n nO P O P O Pl l s s location orientation size e e e E w Markov Random Field Clique potential Clique weight
- 24. • Background and Intuition • Mutual Context of Object and Human Pose Model Representation Model Learning Model Inference • Experiments • Conclusion Outline 24
- 25. 25 Model Learning H O A fO f1 f2 fN P1 P2 PN e e e E w cricket shot cricket bowling Input: Goals: Hidden human poses
- 26. 26 Model Learning H O A fO f1 f2 fN P1 P2 PN Input: Goals: Hidden human poses Structural connectivity e e e E w cricket shot cricket bowling
- 27. e e e E w 27 Model Learning Goals: Hidden human poses Structural connectivity Potential parameters Potential weights H O A fO f1 f2 fN P1 P2 PN Input: cricket shot cricket bowling
- 28. 28 Model Learning Goals: Parameter estimation Hidden variables Structure learning H O A fO f1 f2 fN P1 P2 PN Input: e e e E w cricket shot cricket bowling Hidden human poses Structural connectivity Potential parameters Potential weights
- 29. 29 Model Learning Goals: H O A fO f1 f2 fN P1 P2 PN Approach: croquet shot e e e E w Hidden human poses Structural connectivity Potential parameters Potential weights
- 30. 30 Model Learning Goals: H O A fO f1 f2 fN P1 P2 PN Approach: 2 2 max 2 e eeE e E w Joint density of the model Gaussian priori of the edge number Hill-climbing e e e E w Hidden human poses Structural connectivity Potential parameters Potential weights
- 31. 31 Model Learning Goals: H O A fO f1 f2 fN P1 P2 PN Approach: ( , )e O H( , )e A O ( , )e A H ( , )e nO P ( , )e m nP P( , )e nH P ( , )e OO f ( , )ne n PP f • Maximum likelihood • Standard AdaBoost e e e E w Hidden human poses Structural connectivity Potential parameters Potential weights
- 32. 32 Model Learning Goals: H O A fO f1 f2 fN P1 P2 PN Approach: Max-margin learning 2 2, 1 min 2 r i r i w w • xi: Potential values of the i-th image. • wr: Potential weights of the r-th pose. • y(r): Activity of the r-th pose. • ξi: A slack variable for the i-th image. Notations s.t. , where , 1 , 0 i i c i r i i i i r y r y c i w x w x e e e E w Hidden human poses Structural connectivity Potential parameters Potential weights
- 35. • Background and Intuition • Mutual Context of Object and Human Pose Model Representation Model Learning Model Inference • Experiments • Conclusion Outline 35
- 36. I 36 Model Inference The learned models
- 37. I 37 Model Inference The learned models Head detection Torso detection Tennis racket detection Layout of the object and body parts. Compositional Inference [Chen et al, 2007] * * 1 1 1 1,, , , n n A H O P
- 38. I 38 Model Inference The learned models * * 1 1 1 1,, , , n n A H O P * * ,, , ,K K K K n n A H O P Output
- 39. • Background and Intuition • Mutual Context of Object and Human Pose Model Representation Model Learning Model Inference • Experiments • Conclusion Outline 39
- 40. 40 Dataset and Experiment Setup • Object detection; • Pose estimation; • Activity classification. Tasks: [Gupta et al, 2009] Cricket defensive shot Cricket bowling Croquet shot Tennis forehand Tennis serve Volleyball smash Sport data set: 6 classes 180 training (supervised with object and part locations) & 120 testing images
- 41. [Gupta et al, 2009] Cricket defensive shot Cricket bowling Croquet shot Tennis forehand Tennis serve Volleyball smash Sport data set: 6 classes 41 Dataset and Experiment Setup • Object detection; • Pose estimation; • Activity classification. Tasks: 180 training (supervised with object and part locations) & 120 testing images
- 42. 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 Recall Precision Object Detection Results Cricket bat 42 Valid region Croquet mallet Tennis racket Volleyball 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 Recall Precision Cricket ball Our Method Sliding window Pedestrian context [Andriluka et al, 2009] [Dalal & Triggs, 2006]
- 43. Object Detection Results 43 43 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 Recall Precision Volleyball 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 Recall Precision Cricket ball 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 RecallPrecision Our Method Pedestrian as context Scanning window detector 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 Recall Precision Our Method Pedestrian as context Scanning window detector 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 Recall Precision Our Method Pedestrian as context Scanning window detector Sliding window Pedestrian context Our method SmallobjectBackgroundclutter
- 44. 44 Dataset and Experiment Setup • Object detection; • Pose estimation; • Activity classification. Tasks: [Gupta et al, 2009] Cricket defensive shot Cricket bowling Croquet shot Tennis forehand Tennis serve Volleyball smash Sport data set: 6 classes 180 training & 120 testing images
- 45. 45 Human Pose Estimation Results Method Torso Upper Leg Lower Leg Upper Arm Lower Arm Head Ramanan, 2006 .52 .22 .22 .21 .28 .24 .28 .17 .14 .42 Andriluka et al, 2009 .50 .31 .30 .31 .27 .18 .19 .11 .11 .45 Our full model .66 .43 .39 .44 .34 .44 .40 .27 .29 .58
- 46. 46 Human Pose Estimation Results Method Torso Upper Leg Lower Leg Upper Arm Lower Arm Head Ramanan, 2006 .52 .22 .22 .21 .28 .24 .28 .17 .14 .42 Andriluka et al, 2009 .50 .31 .30 .31 .27 .18 .19 .11 .11 .45 Our full model .66 .43 .39 .44 .34 .44 .40 .27 .29 .58 Andriluka et al, 2009 Our estimation result Tennis serve model Andriluka et al, 2009 Our estimation result Volleyball smash model
- 47. 47 Human Pose Estimation Results Method Torso Upper Leg Lower Leg Upper Arm Lower Arm Head Ramanan, 2006 .52 .22 .22 .21 .28 .24 .28 .17 .14 .42 Andriluka et al, 2009 .50 .31 .30 .31 .27 .18 .19 .11 .11 .45 Our full model .66 .43 .39 .44 .34 .44 .40 .27 .29 .58 One pose per class .63 .40 .36 .41 .31 .38 .35 .21 .23 .52 Estimation result Estimation result Estimation result Estimation result
- 48. 48 Dataset and Experiment Setup • Object detection; • Pose estimation; • Activity classification. Tasks: [Gupta et al, 2009] Cricket defensive shot Cricket bowling Croquet shot Tennis forehand Tennis serve Volleyball smash Sport data set: 6 classes 180 training & 120 testing images
- 49. Activity Classification Results 49 Gupta et al, 2009 Our model Bag-of- Words 83.3% Classificationaccuracy 78.9% 52.5% 0.9 0.8 0.7 0.6 0.5 No scene information Scene is critical!! Cricket shot Tennis forehand Bag-of-words SIFT+SVM Gupta et al, 2009 Our model
- 50. 50 Conclusion Human-Object Interaction Next Steps Vs. • Pose estimation & Object detection on PPMI images. • Modeling multiple objects and humans. Grouplet representation Mutual context model
- 51. 51