K E C H E N 1 , S H A O G A N G G O N G 1 , T A O X I A N G 1 , C H E N C H A N G E L O Y 2

1 . Q U E E N M A R Y , U N I V E R S I T Y O F L O N D O N2 . T H E C H I N E S E U N I V E R S I T Y O F H O N G K O N G

CUMULATIVE ATTRIBUTE SPACE FOR AGE AND CROWD DENSITY ESTIMATION

CVPR 2013, Portland, Oregon

PROBLEMS

How old are they?

How many persons are in the scene?

What is the head pose (viewing angles) of this person?

A REGRESSION FORMULATIONOriginal images/frames

Facial images

Crowd frames

AAM feature

Segment feature

Edge feature

Texture feature

Feature extraction

Feature space Label space

LabelsLearning the mapping

Regression

CHALLENGE – FEATURE VARIATION

The same age

Extrinsic conditions: Lighting conditions; Viewing angles Intrinsic conditions: aging process of different people glasses, hairstyle, gender, ethnicity

Feature

CHALLENGE – FEATURE VARIATION

The same person count

Extrinsic conditions: Lighting conditions; Viewing angles Intrinsic conditions: occlusion, density distribution in the scene

Feature

CHALLENGE – SPARSE AND IMBALANCED DATA

Data distribution of FG-NET Dataset

Max number of samples for each age group is 46

CHALLENGE – SPARSE AND IMBALANCED DATA

Data distribution of UCSD Dataset

RELATED WORKS

• Most focused on feature variation challenge

• Few focused on sparse and imbalanced data challenge

• Two challenges are related

1. Improve feature robustness [Guo et al, CVPR, 2009; Guo et al, TIP, 2012; Ryan et al, DICTA, 2009; Zhang et al, IEEE T ITS, 2011].

2. Improve regressor

[Guo et al, TIP 2008; Chang et al, CVPR 2011; Chao et al, PR 2013; Chan et al, CVPR 2008; Chen et al, BMVC 2012]

OUR APPROACHSolution:• Attribute Learning can address data sparsity problem

--Exploits the shared characteristics between classesHas sematic meaningDiscriminative

Problems:• Applied successfully in classification but not in

regression• How to exploit cumulative dependent nature of

labels in regression?…… …… ……

Age 20 Age 21 Age 60

CUMULATIVE ATTRIBUTE

Age 20

1

1

0

1

…20

0…0

the rest

Cumulative attribute (dependent)

Vs.

0

1

…

20th

0…

0

Non-cumulative attribute (independent)

0

0

LIMITATION OF NON-CUMULATIVE ATTRIBUTE

Age 200

1

…

20th

0…0

Age 6060th0

…

0

0

0

1

…

0…

0

0…0

0

21st0

1

…

0

…

0

0

0

0

Age 21

Age 21

ADVANTAGES OF CUMULATIVE ATTRIBUTE

Age 20

1

1

0

1

…20

0…0

the rest

Age 60

1

1

1

…60

0…

0

1

0

… 1…1 attribute changes

1

1…21

0

…

0

1

1

0

40 attributes change

OUR FRAMEWORK

Imagery Features xi

Facial images Crowd frames

Labels yi

Regression Learning

Cumulative Attributes ai

Feature Extraction

Multi-output Regression Learning

Regression Mapping

Conventional frameworks

1 1 0 0… …1 1 2 yi yi+1 N

JOINT ATTRIBUTE LEARNING

• Joint Attribute Learning

with quadratic loss function

• Regression Learning with attribute representation as input is not limited to a specific regression model

min 12‖𝐖‖ 2

𝐹+𝐶∑𝑖=1

N

‖𝐚𝑖𝑇−(𝐱 𝑖

𝑇𝐖+𝐛)‖ 2𝐹

min 12‖𝐰 𝑗‖ 2

2+𝐶∑𝑖=1

N

𝑙𝑜𝑠𝑠(𝑎𝑖𝑗 , 𝑓 𝑗 (𝐱 𝑖))¿¿

COMPARATIVE EVALUATION

Age EstimationCA-SVR: our method; AGES: Geng et al, TPAMI, 2007; RUN: Yan et al, ICCV, 2007; Ranking: Yan et al, ICME, 2007; RED-SVM: Chang et al, ICPR, 2010; LARR: Guo et al, TIP, 2008; MTWGP: Zhang et al, CVPR, 2010; OHRank: Chang et al, CVPR, 2011; SVR: Guo et al, TIP, 2008;

COMPARATIVE EVALUATION

Crowd Counting

CA-RR: our method; LSSVR: Suykens et al, IJCNN, 2001; KRR: An et al, CVPR, 2007; RFR: Liaw et al, R News, 2002; GPR: Chan et al, CVPR, 2008; RR: Chen et al, BMVC, 2012;

CUMULATIVE (CA) VS. NON-CUMULATIVE (NCA)

Crowd Counting

Age Estimation

ROBUSTNESS AGAINST SPARSE AND IMBALANCED DATA

Age Estimation

Crowd Counting

FEATURE SELECTION BY ATTRIBUTES

Shape plays a more important role than texture when one is younger.

CONCLUSION

• A novel attribute framework for regression

• Exploits cumulative dependent nature of label space

• Effectively addresses sparse and imbalanced data problem

Thanks a lot for your attention! Any questions?

Welcome to our poster 3A-2 for more details.

Ke Chen Shaogang Gong Tao Xiang Chen Change Loy Ph.D student Professor Associate Professor Assistant Professor