Human behaviour analysis is being of great interest in the field of artificial intelligence. Specifically, human action recognition deals with the lowest level of semantic interpretation of meaningful human behaviours, as walking, sitting or falling. In the field of ambient-assisted living, the recognition of such actions at home can support several safety and health care services for the independent living of elderly or impaired people at home. In this sense, this thesis aims to provide valuable advances in vision-based human action recognition for ambient-assisted living scenarios. Initially, a taxonomy is proposed in order to classify the different levels of human behaviour analysis and join existing definitions. Then, a human action recognition method is presented, that is based on fusion of multiple cameras and key pose sequence recognition, and performs in real time. By relying on fusion of multiple views, sufficient correlated data can be obtained despite possible occlusions, noise and unfavourable viewing angles. A visual feature is proposed that only relies on the boundary points of the human silhouette, and does not need the actual RGB colour image. Furthermore, several optimisations and extensions of this method are proposed. In this regard, evolutionary algorithms are employed for the selection of scenario-specific configurations. As a result, the robustness and accuracy of the classification are significantly improved.\linebreak In order to support online learning of such parameters, an adaptive and incremental learning technique is introduced. Last but not least, the presented method is also extended to support the recognition of human actions in continuous video streams. Outstanding results have been obtained on several publicly available datasets achieving the desired robustness required by real-world applications. Therefore, this thesis may pave the way for more advanced human behaviour analysis techniques, such as the recognition of complex activities, personal routines and abnormal behaviour detection.
Vision-based Recognition of Human Behaviour for Intelligent Environments
1. Vision-based Recognition of Human
Behaviour for Intelligent Environments
Alexandros Andre Chaaraoui
Departamento de Tecnolog´ Inform´tica y Computaci´n
ıa
a
o
Universidad de Alicante
alexandros@ua.es
Supervisor: Dr. Francisco Fl´rez-Revuelta
o
January 20, 2014
Alexandros Andre Chaaraoui (UA)
PhD Thesis
January 20, 2014
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5. Introduction
Objectives
Introduction (II)
In this thesis, our goal is to support the development of AAL services
for smart homes with advances in human behaviour analysis.
Main objectives
1
Establish the research framework
2
Propose a method for the recognition of human behaviour
3
Satisfy specific demands of AAL services
4
Reach robustness for different scenarios
Alexandros Andre Chaaraoui (UA)
PhD Thesis
January 20, 2014
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7. Research framework
Related work
Research framework
Proposed taxonomy
Figure 1: Human Behaviour Analysis levels — Classification proposed
in Chaaraoui et al. (2012).
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PhD Thesis
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8. Research framework
Proposal
Conclusions of the analysis
Motion
Motion, pose and gaze estimation is the most resolved level.
Action
Action recognition is currently receiving the greatest interest both from
research and industry.
Activity-Behaviour
Activity and long-term behaviour recognition is performed directly
based on low-level sensor data, instead of using action recognition.
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9. Research framework
Proposal
Camera 1
Long-term
Analysis
Setup
(activities,
inhabitants,
objects…),
Profiles and
Log
Camera 2
...
Camera N
Motion
Detection
Motion
Detection
...
Motion
Detection
Human
Behaviour
Analysis
Human
Behaviour
Analysis
...
Human
Behaviour
Analysis
Multi-view Human Behaviour Analysis
Environmental
Sensor Information
Reasoning
System
Privacy
Event
Alarm
Actuators
Caregiver
Figure 2: Architecture of the intelligent monitoring system to promote
independent living at home and support AAL services.
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13. Contributions
Pose representation
Radial Summary (I)
Figure 4: Overview of the feature extraction process: 1) All the contour points are
assigned to the corresponding radial bin; 2) for each bin, a summary representation
is obtained (example with 18 bins).
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14. Contributions
Pose representation
Radial Summary (II)
Figure 5: Graphical explanation of the statistical range frange of a sample
radial bin.
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15. Contributions
Fusion of multiple views
Fusion of multiple views
Multiple views of the same field of view provide
Additional characteristic data
Advantages with respect to occlusions
Advantages with respect to unfavourable viewing angles
(ambiguous actions)
However, difficulties have been observed
The recognition does not necessarily improve
Performance issues (temporal and spatial)
Burdensome, highly-restricted systems (3D pose estimation,
calibrated and synchronised camera networks, ...)
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PhD Thesis
January 20, 2014
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16. Contributions
Fusion of multiple views
Weighted feature fusion scheme
Weights are learnt for each view and action:
Figure 6: Overview of the feature fusion process of the multi-view pose
representation. This example shows five different views of a specific pose taken
from the walk action class from the IXMAS dataset (Weinland et al., 2006).
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PhD Thesis
January 20, 2014
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17. Contributions
Action classification
Action classification
Key Poses
K Key
Poses
...
...
...
Bag of Key
Poses
K Key
Poses
Sequences of
Key Poses
Figure 7: Outline of the learning stage. Using the pose representations, key
poses are obtained for each action. In this way, a bag-of-key-poses model is
learnt. The temporal relation between key poses is modelled using sequences
of key poses.
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18. Contributions
Action classification
Bag of key poses
Action1
K Key Poses
Action2
K Key Poses
...
...
...
...
ActionA
K Key Poses
Bag of Key Poses
Figure 8: Learning scheme of the bag-of-key-poses model. For each action
class, K key poses are obtained separately and then joined together.
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19. Contributions
Action classification
Recognition
Sequences
of Key Poses
Sequence
Matching
DTW
Action
Recognition
Figure 9: Outline of the recognition stage. The unknown sequence of key
poses is obtained and compared to the known sequences. Through sequence
matching, the action of the video sequence can be recognised.
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January 20, 2014
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20. Contributions
Evolutionary-based optimisation
Evolutionary-based optimisation
Genetic feature subset selection
By means of a genetic algorithm for binary feature selection, the
interesting body parts can be selected, and redundant or noisy body
parts can be ignored.
Figure 10: Example of a result provided by genetic feature selection (dismissed
radial bins are shaded in grey).
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21. Contributions
Evolutionary-based optimisation
Evolutionary-based optimisation
Genetic feature subset selection
Coevolutionary optimisation
Simultaneous selection of training instances, features and
parameter values
Coevolution enables to split the problem in subproblems of
optimisation with a common goal (Wiegand, 2004)
Cooperative coevolution allows to consider intrinsic dependencies
among optimisation goals (Derrac et al., 2012)
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22. Contributions
Evolutionary-based optimisation
Evolutionary-based optimisation
Genetic feature subset selection
Coevolutionary optimisation
Adaptive learning
Evolving bag of key poses
Supports incremental and adaptive learning of new data
Applies selection of training instances, features and parameter
values
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23. Contributions
Continuous recognition
Continuous recognition
In AAL, human action recognition has to be applied to continuous
video streams.
This requires:
To detect meaningful human actions online
And to recognise the appropriate action in real-time
We propose:
To learn action zones, i.e. the most discriminative parts of
action performances
The usage of a sliding and growing window technique for
recognition
Alexandros Andre Chaaraoui (UA)
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24. Contributions
Continuous recognition
Action zones
Figure 11: Sample silhouettes of a waving sequence of the DAI RGBD
dataset. The action zone that should be extracted is highlighted.
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25. Contributions
Continuous recognition
Action zones
Figure 12: Evidence values H(t) of each action class and the detected action
zones are shown for a scratch head sequence of the IXMAS dataset.
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January 20, 2014
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27. Results
Evaluation methodology
Results
Experimentation has been performed on:
Single- and multi-view datasets (up to five views)
Manually- and automatically-extracted silhouettes (including
depth-based segmentation)
Using the following cross validations:
Leave one sequence out (LOSO)
Leave one actor out (LOAO)
Leave one view out (LOVO)
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PhD Thesis
January 20, 2014
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28. Results
Benchmarks
Weizmann dataset
Table 1: Comparison of recognition rates and speeds obtained on the
Weizmann dataset (Gorelick et al., 2007) with other state-of-the-art
approaches.
Approach
˙
Ikizler and Duygulu (2007)
Tran and Sorokin (2008)
Fathi and Mori (2008)
Actions
9
10
10
Test
LOSO
LOSO
LOSO
Rate
100%
100%
100%
fps
N/A
N/A
N/A
Hern´ndez et al. (2011)
a
Cheema et al. (2011)
Sadek et al. (2012)
10
9
10
LOAO
LOSO
LOAO
90.3%
91.6%
97.8%
98
56
18
Our approach
Our approach
10
10
LOSO
LOAO
93.5%
97.8%
188
188
Optimised approach
10
LOAO
100%
210
Alexandros Andre Chaaraoui (UA)
PhD Thesis
January 20, 2014
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29. Results
Benchmarks
MuHAVi-14 dataset
Table 2: Comparison of recognition rates and speeds obtained on the
MuHAVi-14 dataset (Singh et al., 2010) with other state-of-the-art
approaches.
Approach
LOSO
LOAO
LOVO
fps
Singh et al. (2010)
Eweiwi et al. (2011)
82.4%
91.9%
61.8%
77.9%
42.6%
55.8%
N/A
N/A
Cheema et al. (2011)
86.0%
73.5%
50.0%
56
98.5%
94.1%
59.6%
99
100%
100%
-
-
Our approach
Optimised approach
Alexandros Andre Chaaraoui (UA)
PhD Thesis
January 20, 2014
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30. Results
Benchmarks
MuHAVi-8 dataset
Table 3: Comparison of recognition rates and speeds obtained on the
MuHAVi-8 dataset (Singh et al., 2010) with other state-of-the-art approaches.
M´todo
e
LOSO
LOAO
LOVO
fps
Singh et al. (2010)
Mart´
ınez-Contreras et al. (2009)
Eweiwi et al. (2011)
97.8%
98.4%
98.5%
76.4%
85.3%
50.0%
38.2%
N/A
N/A
N/A
Cheema et al. (2011)
95.6%
83.1%
57.4%
56
Our approach
100%
100%
82.4%
98
Alexandros Andre Chaaraoui (UA)
PhD Thesis
January 20, 2014
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31. Results
Benchmarks
IXMAS dataset
Table 4: Comparison with other multi-view human action recognition
approaches of the state of the art. The rates obtained in the LOAO cross
validation performed on the IXMAS dataset are shown.
Approach
Actions
Actors
Views
Rate
fps
Yan et al. (2008)
Wu et al. (2011)
Cilla et al. (2012)
Weinland et al. (2006)
Cilla et al. (2013)
Holte et al. (2012)
11
12
11
11
11
13
12
12
12
10
10
12
4
4
5
5
5
5
78%
89.4%
91.3%
93.3%
94.0%
100%
N/A
N/A
N/A
N/A
N/A
N/A
Cherla et al. (2008)
Weinland et al. (2010)
13
11
N/A
10
4
5
80.1%
83.5%
20
∼500
Our approach
11
12
5
91.4%
207
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PhD Thesis
January 20, 2014
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32. Results
Benchmarks
RGBD datasets
DAI RGBD dataset
Table 5: Cross validation results obtained on our multi-view depth dataset.
Approach
LOSO
LOAO
fps
Our approach
94.4%
100%
80
DHA dataset
Table 6: LOSO cross validation results obtained on the DHA dataset (Lin
et al., 2012) (10 Weizmann actions).
Approach
LOSO
Lin et al. (2012)
Our approach
Alexandros Andre Chaaraoui (UA)
fps
90.8%
N/A
95.2%
99
PhD Thesis
January 20, 2014
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33. Results
Benchmarks
Continuous recognition
Table 7: Obtained results applying CHAR and segment analysis evaluation
(LOAO). Results are detailed using the segmented sequences or the proposed
action zones.
Dataset
Approach
F1 -measure
IXMAS
IXMAS
Segmented sequences
Action zones
0.504
0.705
Weizmann
Weizmann
Segmented sequences
Action zones
0.693
0.928
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PhD Thesis
January 20, 2014
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35. Concluding remarks
Discussion
Discussion
Silhouettes
2D silhouettes can be difficult to obtain and they are view dependant
Privacy
Privacy concerns of indoor monitoring
Intelligent monitoring system with privacy protection
The method only relies on the boundary of the silhouette
Validation of the proposed method
The classification method based on the bag of key poses has also been
validated for gaming and NUI (Chaaraoui et al., 2014, 2013;
Climent-P´rez et al., 2013)
e
Alexandros Andre Chaaraoui (UA)
PhD Thesis
January 20, 2014
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36. Concluding remarks
Conclusions
Conclusions
1
Proposal of a 2D template-based non-parametric method for
human action recognition & optimisations and extensions
2
Specific demands of AAL services have been satisfied: relaxed
camera setup requirements, adaptive learning, continuous
recognition and real-time execution
3
The HAR method based on a bag-of-key-poses model handles
single- and multi-view recognition proficiently.
4
State-of-the-art recognition rates have been achieved,
outperforming the best known rates in several benchmarks.
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PhD Thesis
January 20, 2014
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37. Concluding remarks
Future work
Future work
Future directions of this work:
Pose representation: Other local and global features
Key poses: Generation algorithms
Bag-of-key-poses model: Applications
Distance metrics: Key poses and sequences of key poses
Evaluation and deployment
However, two main future lines stand out:
Recognition of complex activities based on action sequences
and multi-modal data
Feature fusion techniques, e.g. for recognition of subtle movements
or gestures
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January 20, 2014
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38. Other information and details
Other information and details
Alexandros Andre Chaaraoui (UA)
PhD Thesis
January 20, 2014
38 / 45
39. Other information and details
Research projects and grants
Research projects and grants
Intelligent system for follow-up and promotion of personal
autonomy: Computer vision system for the monitoring of
activities of daily living at home – Sistema de visi´n para la
o
monitorizaci´n de la actividad diaria en el hogar. Spanish Ministry of Science and
o
Innovation and Valencian Ministry of Education, Culture and Sport (TALISMAN+,
Technical University of Madrid, University of Deusto, University of Castile-La
Mancha and University of Alicante)
PhD. Research Fellowship – Programa VALi+d para investigadores en
formaci´n. Valencian Ministry of Education, Culture and Sport (ACIF/2011/160)
o
Research Collaboration Stay – Digital Imaging Research Centre, Faculty
of Science, Engineering and Computing, Kingston University. Kingston upon
Thames, UK. (Funded by VALi+d, BEFPI/2013/015)
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PhD Thesis
January 20, 2014
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40. Other information and details
Other activities
Other activities
Teaching collaboration – Information Technology. First year core subject of
Degree in Sound and Image in Telecommunication Engineering
Reviewer of international journals – Neurocomputing (Elsevier),
Pervasive Computing (IEEE), EURASIP Journal on Image and Video Processing
(Springer), Expert Systems With Applications (Elsevier)
Conference session chair – IEEE/RSJ Intelligent Robots and Systems
(IROS 2012), Genetic and Evolutionary Computation Conference (GECCO 2013)
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PhD Thesis
January 20, 2014
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41. Other information and details
Intl. review
International review
This thesis has been approved
by the following reviewers for the
International PhD Honourable Mention
Dr. Jean-Christophe Nebel
(Kingston University, UK)
Dr. Jes´s Mart´
u
ınez del Rinc´n
o
(Queen’s University of Belfast, UK)
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42. Other information and details
Publications
Publications
Journals
I Chaaraoui, A.A., Climent-P´rez, P., Fl´rez-Revuelta, F., 2012. A Review on
e
o
Vision Techniques Applied to Human Behaviour Analysis for Ambient-Assisted
Living. Expert Systems with Applications. Citations: 10
II Chaaraoui, A.A., Climent-P´rez, P., Fl´rez-Revuelta, F., 2013.
e
o
Silhouette-based Human Action Recognition Using Sequences of Key Poses.
Pattern Recognition Letters. Citations: 6
III Chaaraoui, A.A., Fl´rez-Revuelta, F., 2013. Optimizing Human Action
o
Recognition Based on a Cooperative Coevolutionary Algorithm. Engineering
Applications of Artificial Intelligence.
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43. Other information and details
Publications
Publications
IV Chaaraoui, A.A., Padilla-L´pez, J.R., Climent-P´rez, P., Fl´rez-Revuelta, F.,
o
e
o
2014. Evolutionary Joint Selection to Improve Human Action Recognition
with RGB-D Devices. Expert Systems with Applications.
V Chaaraoui, A.A., Fl´rez-Revuelta, F., 2014. A Low-Dimensional Radial
o
Silhouette-based Feature for Fast Human Action Recognition Fusing Multiple
Views. Information Fusion. Under review
VI Chaaraoui, A.A., Fl´rez-Revuelta, F., 2014. Adaptive Human Action
o
Recognition With an Evolving Bag of Key Poses. IEEE Transactions on
Autonomous Mental Development. Under review
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PhD Thesis
January 20, 2014
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44. Other information and details
Publications
Publications
Conferences and workshops
I Intl. Symposium on Ambient Intelligence (ISAmI 2012)
II 3rd Intl. Workshop on Human Behavior Understanding (HBU 2012),
IEEE/RSJ Intl. Conference on Intelligent Robots and Systems (IROS 2012)
III 11th Mexican Intl. Conference on Artificial Intelligence (MICAI 2012)
IV Genetic and Evolutionary Computation Conference (GECCO 2013)
V 5th Intl. Work-conference on Ambient Assisted Living (IWAAL 2013)
VI 3rd Workshop on Consumer Depth Cameras for Computer Vision
(CDC4CV13), IEEE Intl. Conference on Computer Vision (ICCV 2013)
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January 20, 2014
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45. Other information and details
Publications
“We can only see a short distance ahead, but we can see
plenty there that needs to be done.” (Turing, 1950)
Vision-based Recognition of Human Behaviour
for Intelligent Environments
PhD Thesis
Alexandros Andre Chaaraoui
Alexandros Andre Chaaraoui (UA)
PhD Thesis
January 20, 2014
45 / 45
46. References
References
Chaaraoui, A. A., P. Climent-P´rez, and F. Fl´rez-Revuelta (2012). A review on vision
e
o
techniques applied to human behaviour analysis for ambient-assisted living. Expert
Systems with Applications 39 (12), 10873 – 10888.
Chaaraoui, A. A., J. R. Padilla-L´pez, P. Climent-P´rez, and F. Fl´rez-Revuelta (2014).
o
e
o
Evolutionary joint selection to improve human action recognition with RGB-D devices.
Expert Systems with Applications 41 (3), 786 – 794. Methods and Applications of
Artificial and Computational Intelligence.
Chaaraoui, A. A., J. R. Padilla-L´pez, and F. Fl´rez-Revuelta (2013). Fusion of skeletal
o
o
and silhouette-based features for human action recognition with RGB-D devices. In
IEEE 14th International Conference on Computer Vision Workshops, 2013. ICCV
Workshops 2013. To be presented in 3rd Workshop on Consumer Depth Cameras for
Computer Vision (CDC4CV13).
Cheema, S., A. Eweiwi, C. Thurau, and C. Bauckhage (2011). Action recognition by
learning discriminative key poses. In IEEE 13th International Conference on Computer
Vision Workshops, 2011. ICCV Workshops 2011, pp. 1302 –1309.
Cherla, S., K. Kulkarni, A. Kale, and V. Ramasubramanian (2008). Towards fast,
view-invariant human action recognition. In IEEE Conference on Computer Vision and
Pattern Recognition Workshops, 2008. CVPRW 2008, pp. 1 – 8.
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PhD Thesis
January 20, 2014
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47. References
References
Cilla, R., M. A. Patricio, A. Berlanga, and J. M. Molina (2012). A probabilistic,
discriminative and distributed system for the recognition of human actions from multiple
views. Neurocomputing 75 (1), 78 – 87. Brazilian Symposium on Neural Networks (SBRN
2010), International Conference on Hybrid Artificial Intelligence Systems (HAIS 2010).
Cilla, R., M. A. Patricio, A. Berlanga, and J. M. Molina (2013). Human action recognition
with sparse classification and multiple-view learning. Expert Systems. DOI
10.1111/exsy.12040.
Climent-P´rez, P., A. A. Chaaraoui, J. R. Padilla-L´pez, and F. Fl´rez-Revuelta (2013).
e
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Optimal joint selection for skeletal data from RGB-D devices using a genetic algorithm.
In I. Batyrshin and M. Mendoza (Eds.), Advances in Computational Intelligence, Volume
7630 of Lecture Notes in Computer Science, pp. 163 – 174. Springer Berlin / Heidelberg.
Derrac, J., I. Triguero, S. Garc´ and F. Herrera (2012). A co-evolutionary framework for
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human action recognition. In IEEE 13th International Conference on Computer Vision
Workshops, 2011. ICCV Workshops 2011, pp. 1310 –1317.
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January 20, 2014
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References
Fathi, A. and G. Mori (2008). Action recognition by learning mid-level motion features. In
IEEE Conference on Computer Vision and Pattern Recognition, 2008. CVPR 2008, pp.
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shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence 29 (12), 2247
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Holte, M., B. Chakraborty, J. Gonzalez, and T. Moeslund (2012). A local 3-D motion
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Lin, Y.-C., M.-C. Hu, W.-H. Cheng, Y.-H. Hsieh, and H.-M. Chen (2012). Human action
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References
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Weinland, D., M. Ozuysal, and P. Fua (2010). Making action recognition robust to
occlusions and viewpoint changes. In K. Daniilidis, P. Maragos, and N. Paragios (Eds.),
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52. Copyright
Author’s contact details
Alexandros Andre Chaaraoui
alexandros@ua.es
www.alexandrosandre.com
Departamento de Tecnolog´ Inform´tica y Computaci´n
ıa
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o
Universidad de Alicante
Carretera San Vicente del Raspeig s/n
E-03690 San Vicente del Raspeig (Alicante) - Spain
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PhD Thesis
January 20, 2014
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