Human action recognition constitutes a core component of advanced human behavior analysis. The detection and recognition of basic human motion enables to analyze and understand human activities, and to react proactively providing different kinds of services from human-computer interaction to health care assistance. In this paper, a feature-level optimization for human action recognition is proposed. The resulting recognition rate and computational cost are significantly improved by means of a low-dimensional radial summary feature and evolutionary feature subset selection. The introduced feature is computed using only the contour points of human silhouettes. These are spatially aligned based on a radial scheme. This definition shows to be proficient for feature subset selection, since different parts of the human body can be selected by choosing the appropriate feature elements. The best selection is sought using a genetic algorithm. Experimentation has been performed on the publicly available MuHAVi dataset. Promising results are shown, since state-of-the-art recognition rates are considerably outperformed with a highly reduced computational cost.

1. 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)

2. ALEXANDROS ANDRE CHAARAOUI AND
FRANCISCO FLÓREZ-REVUELTA
… …
Amsterdam,
July 6-10, 2013
Geneticand
Evolutionary
Computation
Conference2013

3. ©AlexandrosAndreChaaraouiandFranciscoFlórez-Revuelta(GECCO’13)
Contents
3
1. Introduction
2. Radial Summary Feature
3. Evolutionary Feature Subset
Selection
4. Human Action Recognition Method
5. Experimentation & Results
6. Conclusions
7. References
Q & A and Discussion

4. ©AlexandrosAndreChaaraouiandFranciscoFlórez-Revuelta(GECCO’13)
1. Introduction
4
 Motivation and starting point
Recognition of actions such as walking,
jumping or falling.
Requirements:
High and stable recognition rates
Real-time suitability
Proposal of a visual feature with reduced
extraction cost and low dimensionality
Feature subset selection

5. ©AlexandrosAndreChaaraouiandFranciscoFlórez-Revuelta(GECCO’13)
2. Radial Summary Feature
5
 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. ©AlexandrosAndreChaaraouiandFranciscoFlórez-Revuelta(GECCO’13)
2. Radial Summary Feature
6
Fig 2: Overview of the
proposed Radial Summary

7. ©AlexandrosAndreChaaraouiandFranciscoFlórez-Revuelta(GECCO’13)
3. Evolutionary Feature Subset
Selection7


8. ©AlexandrosAndreChaaraouiandFranciscoFlórez-Revuelta(GECCO’13)
4. Human Action Recognition
Method8

9. ©AlexandrosAndreChaaraouiandFranciscoFlórez-Revuelta(GECCO’13)
4. Human Action Recognition
Method9
 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. ©AlexandrosAndreChaaraouiandFranciscoFlórez-Revuelta(GECCO’13)
4. Human Action Recognition
Method10
 Sequence recognition
 Sequences of key poses
 Nearest-neighbour key poses
 Sequence matching (dynamic time warping)
Fig 3: Sequences of key
poses.

11. ©AlexandrosAndreChaaraouiandFranciscoFlórez-Revuelta(GECCO’13)
5. Experimentation & Results
11
 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
Summary
Feature
Selection
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. ©AlexandrosAndreChaaraouiandFranciscoFlórez-Revuelta(GECCO’13)
5. Experimentation & Results
12
 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. ©AlexandrosAndreChaaraouiandFranciscoFlórez-Revuelta(GECCO’13)
6. Conclusions
13
 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. ©AlexandrosAndreChaaraouiandFranciscoFlórez-Revuelta(GECCO’13)
7. References
14
 [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 Discussion15

16. ALEXANDROS ANDRE CHAARAOUI AND
FRANCISCO FLÓREZ-REVUELTA
… …
Amsterdam,
July 6-10, 2013
Geneticand
Evolutionary
Computation
Conference2013