Amsterdam, The Netherlands July 06-10, 2013

Real World Applications: RWA4.

Room: 02A00 10:40 – 12:20

Session Chair: Alexandros Andre Chaaraoui (University of Alicante, Spain)

ALEXANDROS ANDRE CHAARAOUI AND FRANCISCO FLÓREZ-REVUELTA

HUMAN ACTION RECOGNITION

OPTIMIZATION BASED ON EVOLUTIONARY FEATURE

SUBSET SELECTION

… …

Amsterdam, July 6-10, 2013

Gen

etic

an

d

Evolu

tion

ary

C

om

pu

tatio

n

Con

fere

nce 2

01

3

3

© A

lexandro

s A

ndre

Ch

aara

oui and F

ranci

sco F

lóre

z-R

evu

elt

a

(GEC

CO

’13

)

Contents1. Introduction2. Radial Summary Feature3. Evolutionary Feature Subset

Selection4. Human Action Recognition

Method5. Experimentation & Results6. Conclusions7. ReferencesQ & A and Discussion

4

© A

lexandro

s A

ndre

Ch

aara

oui and F

ranci

sco F

lóre

z-R

evu

elt

a

(GEC

CO

’13

)

1. Introduction

Motivation and starting point Recognition of actions such as walking,

jumping or falling. Requirements:

High and stable recognition ratesReal-time suitability

Proposal of a visual feature with reduced extraction cost and low dimensionality

Feature subset selection

5

© A

lexandro

s A

ndre

Ch

aara

oui and F

ranci

sco F

lóre

z-R

evu

elt

a

(GEC

CO

’13

)

2. Radial Summary Feature

Human Silhouettes Relatively simple extraction

process Rich shape information Contour points

Radial Summary feature proposal Spatial alignment Feature

selection Low dimensionality, reduced

extraction cost, … Fig 1: Sample silhouette of the MuHAVi dataset [1].

6

© A

lexandro

s A

ndre

Ch

aara

oui and F

ranci

sco F

lóre

z-R

evu

elt

a

(GEC

CO

’13

)

2. Radial Summary Feature

Fig 2: Overview of the proposed Radial Summary feature.

7

© A

lexandro

s A

ndre

Ch

aara

oui and F

ranci

sco F

lóre

z-R

evu

elt

a

(GEC

CO

’13

)

3. Evolutionary Feature Subset Selection

Binary selection using a genetic algorithm Binary individual representation:

Active radial bin: uj = 1

Disabled radial bin: uj = 0

Random initial population (but one with all selected)

Fitness based on the evaluation of the feature Individuals with less active bins are favoured One-point crossover combination operator with

ranking selection Flip bit mutation operator Convergence termination criteria

8

© A

lexandro

s A

ndre

Ch

aara

oui and F

ranci

sco F

lóre

z-R

evu

elt

a

(GEC

CO

’13

)

4. Human Action Recognition Method

Pose Representations

Bag-of-Key-Poses Model

Sequences of Key Poses

9

© A

lexandro

s A

ndre

Ch

aara

oui and F

ranci

sco F

lóre

z-R

evu

elt

a

(GEC

CO

’13

)

4. Human Action Recognition Method

Learning based on Bag-of-Key-Poses Model The available pose representations

are reduced to a representative subset of key poses

We use the K-means clustering algorithm

10

© A

lexandro

s A

ndre

Ch

aara

oui and F

ranci

sco F

lóre

z-R

evu

elt

a

(GEC

CO

’13

)

4. Human Action Recognition Method

Sequence recognition Sequences of key poses Nearest-neighbour key poses Sequence matching (dynamic time

warping)

Fig 3: Sequences of key poses.

11

© A

lexandro

s A

ndre

Ch

aara

oui and F

ranci

sco F

lóre

z-R

evu

elt

a

(GEC

CO

’13

)

5. Experimentation & Results

Tested on the MuHAVi-MAS Dataset [1]

Two versions with 14 and 8 actions Manually Annotated Silhouettes Leave-one-actor-out (LOAO) and leave-one-

sequence out (LOSO) cross validations

Dataset Test Chaaraoui et al.

[2]

Radial Summar

y

Feature Selectio

n

State of the Art Rate [3]

MuHAVi-14

LOSO 94.1% 95.6% 98.5% 91.9%

MuHAVi-14

LOAO 86.8% 91.2% 94.1% 77.9%

MuHAVi-8 LOSO 98.5% 100% 100% 98.5%

MuHAVi-8 LOAO 95.6% 97.1% 100% 85.3%

12

© A

lexandro

s A

ndre

Ch

aara

oui and F

ranci

sco F

lóre

z-R

evu

elt

a

(GEC

CO

’13

)

5. Experimentation & Results Result of the feature

selection ~47% feature size

reduction

~14% temporal reduction

96 FPS overall recognition rate Fig 4: Resulting feature subset

selection of the MuHAVi-14 LOSO cross validation test (dismissed radial bins are shaded in gray).

13

© A

lexandro

s A

ndre

Ch

aara

oui and F

ranci

sco F

lóre

z-R

evu

elt

a

(GEC

CO

’13

)

6. Conclusions

Conclusions An evolutionary algorithm has been applied to

optimize action recognition. An appropriate feature for feature subset

selection has been proposed. We demonstrated that a guided selection of

feature elements can improve the recognition rate and reduce the computational cost.

Future work Real-valued weights instead of binary selection Action-class specific feature selection

14

© A

lexandro

s A

ndre

Ch

aara

oui and F

ranci

sco F

lóre

z-R

evu

elt

a

(GEC

CO

’13

)

7. References

[1] Singh, S., Velastin, S.A., Ragheb, H.: Muhavi: A multicamera human action video dataset for the evaluation of action recognition methods. In: 2010 Seventh IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 48–55 (2010)

[2] Chaaraoui, A.A., Climent-Perez, P., Florez-Revuelta, F.: An Efficient Approach for Multi-view Human Action Recognition based on Bag-of-Key-Poses. In Salah, A., ed.: Human Behavior Understanding. Lecture Notes in Computer Science. Springer Berlin / Heidelberg (2012)

[3] A. Eweiwi, S. Cheema, C. Thurau, and C. Bauckhage. Temporal key poses for human action recognition. In Computer Vision Workshops (ICCV Workshops), IEEE International Conference on, pp. 1310-1317 (2011)

15 Q & A and Discussion

ALEXANDROS ANDRE CHAARAOUI AND FRANCISCO FLÓREZ-REVUELTA

HUMAN ACTION RECOGNITION

OPTIMIZATION BASED ON EVOLUTIONARY FEATURE

SUBSET SELECTION

… …

Amsterdam, July 6-10, 2013

Gen

etic

an

d

Evolu

tion

ary

C

om

pu

tatio

n

Con

fere

nce 2

01

3