Gesture recognition systems
- 2. • Motivation • ProblematicIntroduction • hand gesture analysis approaches • vision based gesture taxonomies and representations Resume • challenges • analysis of existing literatureComparisons • Our proposition: last year PFE of my collegues example conclusion 2/30
- 5. Glove based analysis - glove : give as the finger flexion - sensors mechanical or optical : attached to the glove and act like transducer - sensors magnetic or acoustic : to determine the relative position of the hand vision-based analysis - Three dimensional model of human hand - cameras : matches the images of the hand to the model - Estimating parameters : used to detect a gesture classifications analysis of drawing gestures - Stylus : input device based on the recognition of written text Resume: hand gesture analysis approaches 5/30
- 6. the use of wi controller independent of the target system detecting normal gesture using an accelerometer a user- intuitive system that represent a multimodal of personalized gestures . Resume: hand gesture analysis approaches: 6/30
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- 8. Resume: hand gesture analysis approaches: 8/30
- 9. Resume: hand gesture analysis approaches: 9/30
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- 12. • A way for computers to understand human body language • Enables humans to interface with machines and interact naturally without any mechanical devices • Interpreting human gestures via mathematical algorithms 12/30
- 14. Emblems : Familiar gestures that vary across different cultures (ex: the sign of « ok ») Regulators : Regulate, modulate and maintain the flow of speech during a conversation (Ex: interruption) Adaptors Postural changes and other movements frequently made to feel more comfortable (ex : legs and arms position Affective display : Body movements that display a certain affective state (ex : emotions) Illustrators : Pointing to something that you are discussing about. It reinforces what you are saying. 14/30
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- 16. User interface display Standard web camera user A basic working of the gesture recognition 16/30
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- 18. 3D model-based algorithms Appearance-based models 18/30
- 19. 3D model based : • Used heavely for computer animation • Created of 3D complicated surfaces Inconvenient : Use heavy 3D data Advantage : Update the model parameters 19/30
- 20. 2. Apprearance based model: • Performs an average shape from point sets • Sequences as gesture templates. (Parameters for this method are either the images themselves) 20/30
- 21. • Hidden Markov Model (HMM) : is a joint statistical model for an ordered sequence of variables. • Finite-state machine (FSM) : development tool for approaching and solving problems and as a formal way of describing solutions for later developers 21/30
- 22. • K-means : This classification searches for statistically similar groups in multi-spectral space. • K-nearest neighbors (K-NN) : classify objects according to the closest training examples in the feature space. • Template matching : determine similarities between two entities (points, cures, or shapes) • Support vector machine (SVM) : to nonlinearly map input data to where the data to separate them. • Dynamic time warping (DTW) : used to find the optimal alignment of two signals by calculating the distance between each possible pair of points out of two signals 22/30
- 23. Virtual controllers:- Remote control:- Through the use of gesture recognition, remote control with the wave of a hand of various devices is possible. For systems where the act of finding or acquiring a physical controller could require too much time, gestures can be used as an alternative control mechanism. Controlling secondary devices in a car, or controlling a television set are examples of such usage. 23/30
- 24. Socially assistive robotics : Sign language recognition: By using proper sensors worn on the body of a patient and by reading the values from those sensors, robots can assist in patient rehabilitation. The best example can be stroke rehabilitation. Just as speech recognition can transcribe speech to text, certain types of gesture recognition software can transcribe the symbols represented through sign language into text. 24/30
- 25. Handle a degree of freedom: huge variability of 2D appearance depends on camera view point , different silhouette scale and many resolution for temporal dimension . The trade off between : performance , cost , according balancing to the type of the application , real time performance scalability and user independence The ability to analyze images of different hand gestures under different lighting and background conditions 25/30
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- 28. Representation technique : based on computer- aided design through a wired model of the object Difficulty : the system can handle only a limited number of shapes Advantage: allows for real-time object representation along with minimal computing effort 3D model Representation technique : segments the potential region with an object of interest from the given input sequence. Difficulty : sensitive to viewpoint changes and thus cannot provide precise spatial information Advantage: uses global and local feature extraction approaches (has high precision) appearance • For the products mentioned previously it should be modified in terms of cost- effectiveness, robustness, and user acceptability • these commercial products are still in the initial phases. 28/30
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