Tracking Articulated Motion Using a Mixture of Autoregressive Models 1st edition by Ankur Agarwal, Bill Triggs ISBN 3540219828 9783540219828

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ISBN 10:       3540219828
ISBN 13: 978-3540219828
Author:         Ankur Agarwal, Bill Triggs
 

Tracking Articulated Motion Using a Mixture of Autoregressive Models 1st Table of contents:

1. Introduction

   • 1.1 Motivation for Tracking Articulated Motion
   • 1.2 Challenges in Articulated Motion Tracking
   • 1.3 Mixture of Autoregressive Models: Concept and Advantages
   • 1.4 Contributions of the Paper
   • 1.5 Structure of the Paper

2. Related Work

   • 2.1 Overview of Articulated Motion Tracking
   • 2.2 Traditional Techniques for Articulated Tracking (e.g., kinematic models, Kalman filters)
   • 2.3 Autoregressive Models in Time Series Analysis
   • 2.4 Mixture Models and Their Use in Motion Tracking
   • 2.5 Limitations of Existing Tracking Approaches

3. Problem Definition

   • 3.1 Defining Articulated Motion in Computer Vision
   • 3.2 Model Formulation for Tracking Articulated Motion
   • 3.3 The Role of Mixture Autoregressive Models in Motion Tracking
   • 3.4 Assumptions and Constraints in Tracking Articulated Objects

4. Mixture of Autoregressive Models

   • 4.1 Overview of Autoregressive Models
   • 4.2 Mixture Models: Concept and Mathematical Foundation
   • 4.3 Constructing the Mixture Model for Articulated Motion
   • 4.4 Training the Mixture of Autoregressive Models
   • 4.5 Model Selection and Evaluation for Tracking

5. Tracking Articulated Motion

   • 5.1 Tracking Framework: Overview and Architecture
   • 5.2 Using Autoregressive Models for Motion Prediction
   • 5.3 Estimating the States of Articulated Models
   • 5.4 Fitting the Mixture Model to Observed Data
   • 5.5 Handling Complex Motion Patterns and Non-linearity
   • 5.6 Dealing with Occlusions and Measurement Noise

6. Algorithm Design and Optimization

   • 6.1 Optimization Techniques for Model Parameters
   • 6.2 Efficient Computation of Autoregressive Parameters
   • 6.3 Temporal Smoothing and Filtering for Accurate Tracking
   • 6.4 Model Adaptation to New Motion Patterns
   • 6.5 Real-Time Processing Considerations

7. Experimental Setup and Evaluation

   • 7.1 Datasets Used for Articulated Motion Tracking
   • 7.2 Performance Metrics for Tracking Accuracy (e.g., tracking error, robustness)
   • 7.3 Experimental Setup: Hardware and Software Platforms
   • 7.4 Baseline Methods for Comparison (e.g., conventional tracking algorithms)
   • 7.5 Comparison of the Mixture of AR Models with Other Techniques

8. Results and Discussion

   • 8.1 Quantitative Evaluation of Tracking Performance
   • 8.2 Qualitative Results: Visual Comparisons of Tracking Accuracy
   • 8.3 Handling Complex and Nonlinear Motion Patterns
   • 8.4 Robustness to Occlusion, Noise, and Articulated Joints
   • 8.5 Performance in Real-World Scenarios (e.g., human motion tracking)
   • 8.6 Limitations and Areas for Improvement

9. Applications of Articulated Motion Tracking

   • 9.1 Human Motion Capture and Analysis
   • 9.2 Robotic Arm and Manipulation Tracking
   • 9.3 Augmented Reality and Virtual Reality
   • 9.4 Biomechanics and Medical Applications
   • 9.5 Surveillance and Security Systems

10. Challenges and Future Directions

• 10.1 Scalability to High-Dimensional and Large-Scale Data
• 10.2 Improving Robustness in Cluttered and Dynamic Environments
• 10.3 Real-Time Articulated Motion Tracking
• 10.4 Deep Learning Approaches for Joint Feature Learning
• 10.5 Extending to Multi-Object and Multi-Person Tracking
• 10.6 Future Research Directions in Mixture Models for Motion Tracking

11. Conclusion

• 11.1 Summary of Contributions
• 11.2 Effectiveness of Mixture Autoregressive Models for Articulated Motion
• 11.3 Final Thoughts and Outlook for Future Research

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