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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.
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Vision-based Recognition of HumanBehaviour for Intelligent Environments
Alexandros Andre Chaaraoui
Departamento de Tecnologıa Informatica y ComputacionUniversidad de Alicante
Supervisor: Dr. Francisco Florez-Revuelta
January 20, 2014
Alexandros Andre Chaaraoui (UA) PhD Thesis January 20, 2014 1 / 45
Overview
1 Introduction
2 Research framework
3 Contributions
4 Results
5 Concluding remarks
Alexandros Andre Chaaraoui (UA) PhD Thesis January 20, 2014 2 / 45
Introduction
Introduction
Alexandros Andre Chaaraoui (UA) PhD Thesis January 20, 2014 3 / 45
Introduction Motivation
Introduction (I)
Motivation
Demographic ageing - Ambient-assisted living (AAL)
Intelligent environments
Human behaviour analysis
Alexandros Andre Chaaraoui (UA) PhD Thesis January 20, 2014 4 / 45
Introduction Objectives
Introduction (II)
In this thesis, our goal is to support the development of AAL servicesfor 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 5 / 45
Research framework
Research framework
Alexandros Andre Chaaraoui (UA) PhD Thesis January 20, 2014 6 / 45
Research framework Related work
Research framework
Proposed taxonomy
Figure 1: Human Behaviour Analysis levels — Classification proposedin Chaaraoui et al. (2012).
Alexandros Andre Chaaraoui (UA) PhD Thesis January 20, 2014 7 / 45
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 fromresearch and industry.
Activity-Behaviour
Activity and long-term behaviour recognition is performed directlybased on low-level sensor data, instead of using action recognition.
Alexandros Andre Chaaraoui (UA) PhD Thesis January 20, 2014 8 / 45
Research framework Proposal
Camera 1
Motion Detection
Human Behaviour Analysis
Multi-view Human Behaviour Analysis
Privacy
Reasoning System
Alarm Actuators
Camera 2
Motion Detection
Human Behaviour Analysis
Camera N
Motion Detection
Human Behaviour Analysis
...
Setup (activities,
inhabitants, objects…),
Profiles andLog
Event
Long-term Analysis
...
...
Environmental Sensor Information
Caregiver
Figure 2: Architecture of the intelligent monitoring system to promoteindependent living at home and support AAL services.
Alexandros Andre Chaaraoui (UA) PhD Thesis January 20, 2014 9 / 45
Contributions
Contributions
Alexandros Andre Chaaraoui (UA) PhD Thesis January 20, 2014 10 / 45
Contributions Outline
Contributions
Proposals have been made at different processing stages:
1 Pose representation
2 Fusion of multiple views
3 Action classification
4 Evolutionary-based optimisation
5 Continuous recognition
Alexandros Andre Chaaraoui (UA) PhD Thesis January 20, 2014 11 / 45
Contributions Pose representation
Pose representation
Pose
Representations
Silhouettes
Images
Figure 3: Outline of the pose representation process. Based on the recordedvideo frames, foreground segmentations are obtained. Holistic features canthen be extracted relying on the shape of the human silhouettes.
Alexandros Andre Chaaraoui (UA) PhD Thesis January 20, 2014 12 / 45
Contributions Pose representation
Radial Summary (I)
Figure 4: Overview of the feature extraction process: 1) All the contour points areassigned to the corresponding radial bin; 2) for each bin, a summary representationis obtained (example with 18 bins).
Alexandros Andre Chaaraoui (UA) PhD Thesis January 20, 2014 13 / 45
Contributions Pose representation
Radial Summary (II)
Figure 5: Graphical explanation of the statistical range frange of a sampleradial bin.
Alexandros Andre Chaaraoui (UA) PhD Thesis January 20, 2014 14 / 45
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, ...)
Alexandros Andre Chaaraoui (UA) PhD Thesis January 20, 2014 15 / 45
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 poserepresentation. This example shows five different views of a specific pose takenfrom the walk action class from the IXMAS dataset (Weinland et al., 2006).
Alexandros Andre Chaaraoui (UA) PhD Thesis January 20, 2014 16 / 45
Contributions Action classification
Action classification
Key Poses
K Key Poses...
...
...K Key Poses
Bag of Key
Poses
Sequences of
Key Poses
Figure 7: Outline of the learning stage. Using the pose representations, keyposes are obtained for each action. In this way, a bag-of-key-poses model islearnt. The temporal relation between key poses is modelled using sequencesof key poses.
Alexandros Andre Chaaraoui (UA) PhD Thesis January 20, 2014 17 / 45
Contributions Action classification
Bag of key poses
K Key Poses
Bag of Key Poses
Action1
ActionA
... ...
...
...
K Key PosesAction2
K Key Poses
Figure 8: Learning scheme of the bag-of-key-poses model. For each actionclass, K key poses are obtained separately and then joined together.
Alexandros Andre Chaaraoui (UA) PhD Thesis January 20, 2014 18 / 45
Contributions Action classification
Recognition
Sequences
of Key Poses
Sequence
Matching
Action
Recognition
DTW
Figure 9: Outline of the recognition stage. The unknown sequence of keyposes is obtained and compared to the known sequences. Through sequencematching, the action of the video sequence can be recognised.
Alexandros Andre Chaaraoui (UA) PhD Thesis January 20, 2014 19 / 45
Contributions Evolutionary-based optimisation
Evolutionary-based optimisation
Genetic feature subset selection
By means of a genetic algorithm for binary feature selection, theinteresting body parts can be selected, and redundant or noisy bodyparts can be ignored.
Figure 10: Example of a result provided by genetic feature selection (dismissed
radial bins are shaded in grey).
Alexandros Andre Chaaraoui (UA) PhD Thesis January 20, 2014 20 / 45
Contributions Evolutionary-based optimisation
Evolutionary-based optimisation
Genetic feature subset selection
Coevolutionary optimisation
Simultaneous selection of training instances, features andparameter values
Coevolution enables to split the problem in subproblems ofoptimisation with a common goal (Wiegand, 2004)
Cooperative coevolution allows to consider intrinsic dependenciesamong optimisation goals (Derrac et al., 2012)
Alexandros Andre Chaaraoui (UA) PhD Thesis January 20, 2014 21 / 45
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 parametervalues
Alexandros Andre Chaaraoui (UA) PhD Thesis January 20, 2014 22 / 45
Contributions Continuous recognition
Continuous recognition
In AAL, human action recognition has to be applied to continuousvideo 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 ofaction performances
The usage of a sliding and growing window technique forrecognition
Alexandros Andre Chaaraoui (UA) PhD Thesis January 20, 2014 23 / 45
Contributions Continuous recognition
Action zones
Figure 11: Sample silhouettes of a waving sequence of the DAI RGBDdataset. The action zone that should be extracted is highlighted.
Alexandros Andre Chaaraoui (UA) PhD Thesis January 20, 2014 24 / 45
Contributions Continuous recognition
Action zones
Figure 12: Evidence values H(t) of each action class and the detected actionzones are shown for a scratch head sequence of the IXMAS dataset.
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Results
Results
Alexandros Andre Chaaraoui (UA) PhD Thesis January 20, 2014 26 / 45
Results Evaluation methodology
Results
Experimentation has been performed on:
Single- and multi-view datasets (up to five views)
Manually- and automatically-extracted silhouettes (includingdepth-based segmentation)
Using the following cross validations:
Leave one sequence out (LOSO)
Leave one actor out (LOAO)
Leave one view out (LOVO)
Alexandros Andre Chaaraoui (UA) PhD Thesis January 20, 2014 27 / 45
Results Benchmarks
Weizmann dataset
Table 1: Comparison of recognition rates and speeds obtained on theWeizmann dataset (Gorelick et al., 2007) with other state-of-the-art
approaches.
Approach Actions Test Rate fps
Ikizler and Duygulu (2007) 9 LOSO 100% N/ATran and Sorokin (2008) 10 LOSO 100% N/AFathi and Mori (2008) 10 LOSO 100% N/A
Hernandez et al. (2011) 10 LOAO 90.3% 98Cheema et al. (2011) 9 LOSO 91.6% 56Sadek et al. (2012) 10 LOAO 97.8% 18
Our approach 10 LOSO 93.5% 188Our approach 10 LOAO 97.8% 188
Optimised approach 10 LOAO 100% 210
Alexandros Andre Chaaraoui (UA) PhD Thesis January 20, 2014 28 / 45
Results Benchmarks
MuHAVi-14 dataset
Table 2: Comparison of recognition rates and speeds obtained on theMuHAVi-14 dataset (Singh et al., 2010) with other state-of-the-art
approaches.
Approach LOSO LOAO LOVO fps
Singh et al. (2010) 82.4% 61.8% 42.6% N/AEweiwi et al. (2011) 91.9% 77.9% 55.8% N/A
Cheema et al. (2011) 86.0% 73.5% 50.0% 56
Our approach 98.5% 94.1% 59.6% 99
Optimised approach 100% 100% - -
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Results Benchmarks
MuHAVi-8 dataset
Table 3: Comparison of recognition rates and speeds obtained on theMuHAVi-8 dataset (Singh et al., 2010) with other state-of-the-art approaches.
Metodo LOSO LOAO LOVO fps
Singh et al. (2010) 97.8% 76.4% 50.0% N/AMartınez-Contreras et al. (2009) 98.4% - - N/AEweiwi et al. (2011) 98.5% 85.3% 38.2% 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 30 / 45
Results Benchmarks
IXMAS dataset
Table 4: Comparison with other multi-view human action recognitionapproaches 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) 11 12 4 78% N/AWu et al. (2011) 12 12 4 89.4% N/ACilla et al. (2012) 11 12 5 91.3% N/AWeinland et al. (2006) 11 10 5 93.3% N/ACilla et al. (2013) 11 10 5 94.0% N/AHolte et al. (2012) 13 12 5 100% N/A
Cherla et al. (2008) 13 N/A 4 80.1% 20Weinland et al. (2010) 11 10 5 83.5% ∼500
Our approach 11 12 5 91.4% 207
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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 (Linet al., 2012) (10 Weizmann actions).
Approach LOSO fps
Lin et al. (2012) 90.8% N/A
Our approach 95.2% 99
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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 Segmented sequences 0.504IXMAS Action zones 0.705
Weizmann Segmented sequences 0.693Weizmann Action zones 0.928
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Concluding remarks
Concluding remarks
Alexandros Andre Chaaraoui (UA) PhD Thesis January 20, 2014 34 / 45
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 beenvalidated for gaming and NUI (Chaaraoui et al., 2014, 2013;Climent-Perez et al., 2013)
Alexandros Andre Chaaraoui (UA) PhD Thesis January 20, 2014 35 / 45
Concluding remarks Conclusions
Conclusions
1 Proposal of a 2D template-based non-parametric method forhuman action recognition & optimisations and extensions
2 Specific demands of AAL services have been satisfied: relaxedcamera setup requirements, adaptive learning, continuousrecognition and real-time execution
3 The HAR method based on a bag-of-key-poses model handlessingle- and multi-view recognition proficiently.
4 State-of-the-art recognition rates have been achieved,outperforming the best known rates in several benchmarks.
Alexandros Andre Chaaraoui (UA) PhD Thesis January 20, 2014 36 / 45
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 sequencesand multi-modal data
Feature fusion techniques, e.g. for recognition of subtle movementsor gestures
Alexandros Andre Chaaraoui (UA) PhD Thesis January 20, 2014 37 / 45
Other information and details
Other information and details
Alexandros Andre Chaaraoui (UA) PhD Thesis January 20, 2014 38 / 45
Other information and details Research projects and grants
Research projects and grants
Intelligent system for follow-up and promotion of personalautonomy: Computer vision system for the monitoring ofactivities of daily living at home – Sistema de vision para la
monitorizacion de la actividad diaria en el hogar. Spanish Ministry of Science and
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
formacion. Valencian Ministry of Education, Culture and Sport (ACIF/2011/160)
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)
Alexandros Andre Chaaraoui (UA) PhD Thesis January 20, 2014 39 / 45
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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Other information and details Intl. review
International review
This thesis has been approvedby the following reviewers for the
International PhD Honourable Mention
Dr. Jean-Christophe Nebel(Kingston University, UK)
Dr. Jesus Martınez del Rincon(Queen’s University of Belfast, UK)
Alexandros Andre Chaaraoui (UA) PhD Thesis January 20, 2014 41 / 45
Other information and details Publications
Publications
Journals
I Chaaraoui, A.A., Climent-Perez, P., Florez-Revuelta, F., 2012. A Review on
Vision Techniques Applied to Human Behaviour Analysis for Ambient-Assisted
Living. Expert Systems with Applications. Citations: 10
II Chaaraoui, A.A., Climent-Perez, P., Florez-Revuelta, F., 2013.
Silhouette-based Human Action Recognition Using Sequences of Key Poses.
Pattern Recognition Letters. Citations: 6
III Chaaraoui, A.A., Florez-Revuelta, F., 2013. Optimizing Human Action
Recognition Based on a Cooperative Coevolutionary Algorithm. Engineering
Applications of Artificial Intelligence.
Alexandros Andre Chaaraoui (UA) PhD Thesis January 20, 2014 42 / 45
Other information and details Publications
Publications
IV Chaaraoui, A.A., Padilla-Lopez, J.R., Climent-Perez, P., Florez-Revuelta, F.,
2014. Evolutionary Joint Selection to Improve Human Action Recognition
with RGB-D Devices. Expert Systems with Applications.
V Chaaraoui, A.A., Florez-Revuelta, F., 2014. A Low-Dimensional Radial
Silhouette-based Feature for Fast Human Action Recognition Fusing Multiple
Views. Information Fusion. Under review
VI Chaaraoui, A.A., Florez-Revuelta, F., 2014. Adaptive Human Action
Recognition With an Evolving Bag of Key Poses. IEEE Transactions on
Autonomous Mental Development. Under review
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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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Other information and details Publications
“We can only see a short distance ahead, but we can seeplenty there that needs to be done.” (Turing, 1950)
Vision-based Recognition of Human Behaviourfor Intelligent Environments
PhD Thesis
Alexandros Andre Chaaraoui
Alexandros Andre Chaaraoui (UA) PhD Thesis January 20, 2014 45 / 45
References
References
Chaaraoui, A. A., P. Climent-Perez, and F. Florez-Revuelta (2012). A review on visiontechniques applied to human behaviour analysis for ambient-assisted living. ExpertSystems with Applications 39 (12), 10873 – 10888.
Chaaraoui, A. A., J. R. Padilla-Lopez, P. Climent-Perez, and F. Florez-Revuelta (2014).Evolutionary joint selection to improve human action recognition with RGB-D devices.Expert Systems with Applications 41 (3), 786 – 794. Methods and Applications ofArtificial and Computational Intelligence.
Chaaraoui, A. A., J. R. Padilla-Lopez, and F. Florez-Revuelta (2013). Fusion of skeletaland silhouette-based features for human action recognition with RGB-D devices. InIEEE 14th International Conference on Computer Vision Workshops, 2013. ICCVWorkshops 2013. To be presented in 3rd Workshop on Consumer Depth Cameras forComputer Vision (CDC4CV13).
Cheema, S., A. Eweiwi, C. Thurau, and C. Bauckhage (2011). Action recognition bylearning discriminative key poses. In IEEE 13th International Conference on ComputerVision Workshops, 2011. ICCV Workshops 2011, pp. 1302 –1309.
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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 multipleviews. Neurocomputing 75 (1), 78 – 87. Brazilian Symposium on Neural Networks (SBRN2010), International Conference on Hybrid Artificial Intelligence Systems (HAIS 2010).
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Climent-Perez, P., A. A. Chaaraoui, J. R. Padilla-Lopez, and F. Florez-Revuelta (2013).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, Volume7630 of Lecture Notes in Computer Science, pp. 163 – 174. Springer Berlin / Heidelberg.
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Lin, Y.-C., M.-C. Hu, W.-H. Cheng, Y.-H. Hsieh, and H.-M. Chen (2012). Human actionrecognition and retrieval using sole depth information. In Proceedings of the 20th ACMinternational conference on Multimedia, MM ’12, New York, NY, USA, pp. 1053 – 1056.ACM.
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Copyright
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Author’s contact details
Alexandros Andre [email protected]
www.alexandrosandre.com
Departamento de Tecnologıa Informatica y ComputacionUniversidad de AlicanteCarretera San Vicente del Raspeig s/nE-03690 San Vicente del Raspeig (Alicante) - Spain
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