Adaptive Probabilistic Visual Tracking with Incremental Subspace Update 1st edition by David Ross, Jongwoo Lim, Ming Hsuan Yang ISBN 3540219835 9783540219835

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ISBN 10:       3540219835
ISBN 13: 978-3540219835
Author:          David Ross, Jongwoo Lim, Ming-Hsuan Yang
 

Adaptive Probabilistic Visual Tracking with Incremental Subspace Update 1st  Table of contents:

• Introduction

  • 1.1 Background and Motivation
  • 1.2 Visual Tracking in Computer Vision
  • 1.3 Challenges in Probabilistic Visual Tracking
  • 1.4 Role of Incremental Subspace Update in Visual Tracking
  • 1.5 Objectives and Contributions of the Paper
  • 1.6 Structure of the Paper

• Related Work

  • 2.1 Overview of Visual Tracking Methods
  • 2.2 Probabilistic Tracking Techniques: Particle Filters, Kalman Filters, etc.
  • 2.3 Subspace Learning and Update Techniques
  • 2.4 Adaptive and Incremental Approaches in Visual Tracking
  • 2.5 Limitations of Existing Tracking Methods

• Fundamentals of Probabilistic Visual Tracking

  • 3.1 Introduction to Probabilistic Models in Tracking
  • 3.2 Markov Models and Bayesian Inference for Tracking
  • 3.3 Particle Filtering and Monte Carlo Methods
  • 3.4 Gaussian Mixture Models (GMM) and Tracking with GMM
  • 3.5 Tracking with Dynamic and Nonlinear Models

• Incremental Subspace Learning and Update

  • 4.1 Introduction to Subspace Methods in Tracking
  • 4.2 Linear Subspace Learning for Object Representation
  • 4.3 Incremental Update Techniques for Subspaces
  • 4.4 Handling Concept Drift and Non-Stationary Data
  • 4.5 Combining Incremental Subspace Learning with Tracking Algorithms

• Adaptive Tracking Framework with Incremental Subspace Update

  • 5.1 Overview of the Proposed Tracking Framework
  • 5.2 Adaptive Model Update Process
  • 5.3 Incremental Subspace Learning for Visual Tracking
  • 5.4 Updating the Subspace Representation Dynamically
  • 5.5 Integration of Probabilistic Models and Subspace Learning
  • 5.6 Handling Noise and Outliers in the Update Process

• Algorithm Implementation

  • 6.1 Input and Initialization: Feature Extraction and Model Setup
  • 6.2 Adaptive Tracking Procedure with Incremental Subspace Updates
  • 6.3 Efficient Computational Techniques for Real-Time Performance
  • 6.4 Implementation Challenges and Solutions
  • 6.5 Algorithm Complexity and Performance Analysis

• Experimental Setup and Evaluation

  • 7.1 Datasets and Tracking Scenarios
  • 7.2 Performance Metrics: Accuracy, Robustness, and Computational Efficiency
  • 7.3 Benchmarking Against State-of-the-Art Tracking Methods
  • 7.4 Results in Various Real-World Tracking Scenarios
  • 7.5 Sensitivity to Parameters and Model Tuning

• Results and Discussion

  • 8.1 Visual Tracking Results: Accuracy and Robustness in Dynamic Scenes
  • 8.2 Comparison of Adaptive Tracking vs. Static Methods
  • 8.3 Impact of Incremental Subspace Updates on Tracking Quality
  • 8.4 Handling Occlusions, Noise, and Scale Variations
  • 8.5 Efficiency in Long-Term Tracking and Real-Time Applications

• Applications

  • 9.1 Surveillance Systems and Object Tracking
  • 9.2 Autonomous Vehicles: Object Detection and Tracking
  • 9.3 Human-Computer Interaction (HCI) and Gesture Recognition
  • 9.4 Augmented Reality (AR) and Virtual Reality (VR) Tracking
  • 9.5 Robotics: Object Manipulation and Motion Prediction

• Challenges and Future Directions

  • 10.1 Scalability to Large-Scale and Complex Environments
  • 10.2 Real-Time Adaptation in Highly Dynamic and Cluttered Scenes
  • 10.3 Improving Robustness to Non-Ideal Conditions (e.g., lighting, motion blur)
  • 10.4 Incorporating Deep Learning for Feature Extraction and Model Adaptation
  • 10.5 Future Research Directions in Adaptive and Incremental Tracking Methods

• Conclusion

  • 11.1 Summary of Contributions and Key Findings
  • 11.2 Practical Implications of the Adaptive Probabilistic Tracking Framework
  • 11.3 Closing Remarks and Future Work

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