Optimal Importance Sampling for Tracking in Image Sequences: Application to Point Tracking 1st edition by Elise Arnaud, Etienne Memin ISBN 3540219828 9783540219828

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Product details:

ISBN 10:       3540219828
ISBN 13: 978-3540219828
Author:         Elise Arnaud, Etienne Mémin
 

Optimal Importance Sampling for Tracking in Image Sequences: Application to Point Tracking 1st Table of contents:

1. Introduction

   • 1.1 Motivation for Tracking in Image Sequences
   • 1.2 Importance Sampling in Tracking
   • 1.3 Applications of Point Tracking in Computer Vision
   • 1.4 Contributions of the Paper
   • 1.5 Structure of the Paper

2. Related Work

   • 2.1 Overview of Tracking Methods in Image Sequences
   • 2.2 Feature Point Tracking Algorithms
   • 2.3 Monte Carlo Methods in Tracking and Computer Vision
   • 2.4 Importance Sampling and Its Use in Dynamic Systems
   • 2.5 Limitations and Gaps in Existing Tracking Approaches

3. Problem Definition

   • 3.1 Point Tracking: Problem Formulation
   • 3.2 Defining the Tracking Model
   • 3.3 Assumptions and Constraints in Point Tracking
   • 3.4 Challenges in Real-Time Tracking and Accuracy

4. Importance Sampling for Tracking

   • 4.1 Basic Concept of Importance Sampling
   • 4.2 Optimal Importance Sampling in Tracking
   • 4.3 The Importance Weighting Scheme
   • 4.4 Estimation and Correction of Tracking States
   • 4.5 Adaptive Importance Sampling in Dynamic Environments

5. Point Tracking Algorithm Using Optimal Importance Sampling

   • 5.1 Overview of the Point Tracking Framework
   • 5.2 Particle Filter-based Point Tracking
   • 5.3 Propagation of Particles and State Estimation
   • 5.4 Likelihood Function for Point Tracking
   • 5.5 Resampling Strategies and Optimal Weighting
   • 5.6 Handling Multiple Points and Occlusions

6. Algorithm Design and Optimization

   • 6.1 Algorithm Design for Efficient Sampling and Tracking
   • 6.2 Speeding Up Tracking with Parallelization
   • 6.3 Real-Time Processing Considerations
   • 6.4 Handling Noise and Measurement Uncertainty
   • 6.5 Optimization Techniques for Performance and Accuracy

7. Experimental Setup and Evaluation

   • 7.1 Datasets Used for Point Tracking
   • 7.2 Performance Metrics for Tracking Accuracy (e.g., Tracking Error, Precision, Recall)
   • 7.3 Experimental Setup: Video Sequences and Test Scenarios
   • 7.4 Comparison with Other Tracking Methods
   • 7.5 Hardware and Software Platforms for Experiments

8. Results and Discussion

   • 8.1 Quantitative Evaluation of Tracking Performance
   • 8.2 Comparison with Existing Point Tracking Techniques
   • 8.3 Analysis of the Importance Sampling Efficiency
   • 8.4 Effectiveness of Optimal Importance Sampling in Noisy Environments
   • 8.5 Visual Comparison of Tracking Results
   • 8.6 Discussion of Strengths and Limitations

9. Applications of Point Tracking with Optimal Importance Sampling

   • 9.1 Object Tracking in Video Surveillance Systems
   • 9.2 Motion Capture and Animation
   • 9.3 Robotics: Visual Servoing and Navigation
   • 9.4 Augmented Reality and Interactive Systems
   • 9.5 Tracking in Autonomous Vehicles and UAVs

10. Challenges and Future Directions

• 10.1 Scaling the Algorithm to Handle Larger Scenes
• 10.2 Improving Robustness to Occlusions and Cluttered Backgrounds
• 10.3 Integration with Deep Learning for Feature Extraction
• 10.4 Real-Time Performance and Hardware Optimization
• 10.5 Extending to Multi-Object and Multi-Feature Tracking
• 10.6 Future Research Directions in Optimal Sampling for Tracking

11. Conclusion

• 11.1 Summary of Contributions
• 11.2 Impact of Optimal Importance Sampling on Point Tracking
• 11.3 Limitations and Possible Improvements
• 11.4 Final Thoughts and Future Outlook

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