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Table of contents
Chapter 1
Introduction
1.1 Problem Statement
1.2 Motivation and objectives
1.3 Sub-problems
1.4 Assumptions
1.5 Scope
1.6 Contribution
1.7 Outline
Chapter 2
Literature Survey
2.1 Introduction
2.2 Intention detection
2.3 Robotic wheelchairs
2.4 Head pose estimation
2.4.1 Model-based solutions
2.4.2 Appearance and feature-based techniques
2.5 Hand gesture recognition
2.6 Conclusion
Chapter 3
Head-Based Intent Recognition
3.1 Introduction
3.2 Pre-processing steps: face detection and tracking
3.2.1 Histogram-based skin colour detection
3.2.2 Adaboost-based skin colour detection
3.2.3 Face detection and localisation
3.2.3.1 Erosion
3.2.3.2 Dilation and connected component labelling
3.2.3.3 Principal Component Analysis
3.3 Recognition of head-based direction intent
3.3.1 Symmetry-based Approach
3.3.2 Centre of Gravity (COG) of the Symmetry Curve
3.3.3 Linear Regression on the Symmetry Curve
3.3.4 Single frame head pose classification
3.3.5 Head rotation detection: Head-based direction intent recognition
3.4 Recognition of head-based speed variation intent
3.5 Adaboost for head-based direction and speed variation recognition
3.5.1 Adaboost face detection
3.5.2 Camshift tracking
3.5.3 Nose template matching
3.6 Conclusion
Chapter 4
Hand-Based Intent Recognition
4.1 Introduction
4.2 Pre-processing steps: Hand detection and tracking
4.3 Recognition of hand-based direction intent
4.3.1 Vertical symmetry-based direction intent recognition
4.3.2 Artificial Neural Networks (Multilayer Perceptron)
4.3.3 Support Vector Machines
4.3.4 K-means clustering
4.3.5 Hand rotation detection: Direction intent recognition
4.3.6 Template-matching-based direction intent recognition
4.4 Recognition of hand-based speed variation intent
4.4.1 Template Matching-based speed variation recognition
4.4.2 Speed variation recognition based on ellipse shaped mask
4.5 Histogram of oriented gradient (HOG) for hand-based speed variation recognition
4.6 Conclusion
Chapter 5
Results and Discussion
5.1 Introduction
5.2 Head-based intent recognition
5.2.1 Performance for the recognition of the head in rotation: direction recognition
5.2.2 Performance for the recognition of the head in vertical motion: speed variation recognition
5.3 Hand-based intent recognition
5.3.1 Performance for the recognition of the hand in rotation: direction recognition
5.3.2 Performance for the recognition of the hand in vertical motion: speed variation recognition
5.4 Extrapolation for data efficiency
5.5 Concluding remarks
Chapter 6
Conclusion
6.1 Summary of contributions
6.2 Concluding remarks
6.3 Future work
