Joint Bayes Filter A Hybrid Tracker for Non-rigid Hand Motion Recognition 1st edition by Huang Fei, Ian Reid ISBN 3540219828 9783540219828

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ISBN 10:       3540219828
ISBN 13: 978-3540219828
Author:         Huang Fei, Ian Reid
 In sign-language or gesture recognition, articulated hand motion tracking is usually a prerequisite to behaviour understanding. However the difficulties such as non-rigidity of the hand, complex background scenes, and occlusion etc make tracking a challenging task. In this paper we present a hybrid HMM/Particle filter tracker for simultaneously tracking and recognition of non-rigid hand motion. By utilising separate image cues, we decompose complex motion into two independent (non-rigid/rigid) components. A generative model is used to explore the intrinsic patterns of the hand articulation. Non-linear dynamics of the articulation such as fast appearance deformation can therefore be tracked without resorting to a complex kinematic model. The rigid motion component is approximated as the motion of a planar region, where a standard particle filter method suffice. The novel contribution of the paper is that we unify the independent treatments of non-rigid motion and rigid motion into a robust Bayesian framework. The efficacy of this method is demonstrated by performing successful tracking in the presence of significant occlusion clutter.

Joint Bayes Filter: A Hybrid Tracker for Non-rigid Hand Motion Recognition 1st Table of contents:

• Introduction

  • 1.1 Background and Motivation
  • 1.2 The Challenge of Non-Rigid Hand Motion Recognition
  • 1.3 Overview of the Joint Bayes Filter Approach
  • 1.4 Contributions of the Paper
  • 1.5 Structure of the Paper

• Related Work

  • 2.1 Hand Motion Tracking and Recognition: Overview of Existing Methods
  • 2.2 Bayes Filtering in Motion Tracking
  • 2.3 Non-Rigid Motion Recognition in Computer Vision
  • 2.4 Hybrid Tracking Methods: Integration of Bayesian Filters with Other Techniques
  • 2.5 Limitations of Current Approaches

• Problem Formulation

  • 3.1 Definition of Non-Rigid Hand Motion Recognition
  • 3.2 Challenges in Hand Motion Tracking (Occlusions, Variability, Real-Time Processing)
  • 3.3 Assumptions and Constraints in the Proposed Hybrid Tracker

• Bayes Filtering and Its Applications in Motion Tracking

  • 4.1 Overview of Bayes Filtering Techniques (Kalman Filter, Particle Filter, etc.)
  • 4.2 Use of Bayesian Framework for State Estimation
  • 4.3 Incorporating Motion Models and Observation Models
  • 4.4 The Role of Uncertainty in Motion Tracking

• Non-Rigid Hand Motion Modeling

  • 5.1 Representation of Non-Rigid Motion for Hand Tracking
  • 5.2 Geometric and Kinematic Models for Hand Motion
  • 5.3 Challenges in Modeling Non-Rigid Deformations
  • 5.4 Using Shape Priors and Deformation Constraints in Hand Motion Recognition

• Joint Bayes Filter: A Hybrid Tracker

  • 6.1 Concept of Joint Bayes Filter for Hybrid Tracking
  • 6.2 Integrating Multiple Observations and Motion Models
  • 6.3 The Hybrid Approach: Combining Kalman/Particle Filters with Non-Rigid Models
  • 6.4 Update and Prediction Steps in the Joint Bayes Filter
  • 6.5 Benefits of the Hybrid Framework in Hand Motion Recognition

• Algorithm Design and Implementation

  • 7.1 Overview of the Hybrid Tracker Algorithm
  • 7.2 Tracking Hand Motion Using the Joint Bayes Filter
  • 7.3 Hand Gesture Recognition and Classification
  • 7.4 Real-Time Processing Considerations
  • 7.5 Implementation Details and Computational Efficiency

• Experimental Setup and Evaluation

  • 8.1 Datasets for Hand Motion Recognition and Tracking
  • 8.2 Evaluation Metrics for Motion Tracking Accuracy
  • 8.3 Experimental Protocols and Benchmarking
  • 8.4 Comparison with State-of-the-Art Hand Tracking Methods
  • 8.5 Evaluation of Robustness in Challenging Scenarios (e.g., Occlusions, Fast Movements)

• Results and Discussion

  • 9.1 Quantitative Results of Hand Motion Tracking
  • 9.2 Qualitative Results: Visual Demonstrations of Hand Motion Recognition
  • 9.3 Performance Comparison Between the Joint Bayes Filter and Other Hybrid Trackers
  • 9.4 Discussion on the Strengths and Weaknesses of the Approach
  • 9.5 Impact of Hybrid Tracking on Recognition Accuracy and Real-Time Processing

• Applications of Joint Bayes Filter in Hand Motion Recognition

  • 10.1 Human-Computer Interaction and Gesture Recognition
  • 10.2 Sign Language Recognition
  • 10.3 Virtual Reality and Augmented Reality Applications
  • 10.4 Robotics and Gesture Control
  • 10.5 Medical and Rehabilitation Applications

• Challenges and Future Directions

  • 11.1 Handling Complex and Extreme Non-Rigid Hand Motions
  • 11.2 Improving Real-Time Performance in Dynamic Environments
  • 11.3 Incorporating Deep Learning for Enhanced Motion Understanding
  • 11.4 Addressing Challenges of Data Scarcity and Annotation
  • 11.5 Future Research Directions in Hybrid Motion Tracking

• Conclusion

  • 12.1 Summary of Contributions and Results
  • 12.2 Practical Implications for Hand Motion Recognition
  • 12.3 Limitations and Areas for Improvement
  • 12.4 Final Remarks and Vision for Future Work

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