Title Details

1 Introduction.

1.1 Overview of the Problem.

1.2 General Tracking System Prototype.

1.3 The Tracking Pipeline.

2 Model Representation.

2.1 Camera Model.

2.2 Object Model.

2.3 Mapping Between Object and Sensor Spaces.

2.4 Object Dynamics.

3 The Visual Modality Abstraction.

3.1 Preprocessing.

3.2 Sampling and Updating Reference Features.

3.3 Model Matching with the Image Data.

3.4 Data Fusion Across Multiple Modalities and Cameras.

4 Examples of Visual Modalities.

4.1 Color Statistics.

4.2 Background Subtraction.

4.3 Blobs.

4.4 Model Contours.

4.5 Keypoints.

4.6 Motion.

4.7 Templates.

5 Recursive State-Space Estimation.

5.1 Target-State Distribution.

5.2 MLE and MAP Estimation.

5.3 Gaussian Filters.

5.4 Monte Carlo Filters.

5.5 Grid Filters.

6 Examples of Target Detectors.

6.1 Blob Clustering.

6.2 AdaBoost Classifiers.

6.3 Geometric Hashing.

6.4 Monte Carlo sampling.

6.5 Invariant Keypoints.

7 Building Applications with OpenTL.

7.1 Functional Architecture of OpenTL.

7.2 Building a Tutorial Application with OpenTL.

7.3 Other Application Examples.

Appendix A: Pose Estimation.

A.1 Point Correspondences.

A.2 Line Correspondences.

A.3 Point and Line Correspondences.

A.4 Computation of the Projective DLT Matrices.

Appendix B: Pose Representations.

B.1 Poses Without Rotation.

B.2 Parametrizing Rotations.

B.3 Poses with Rotation and Uniform Scale.

B.4 Affinity.

B.5 Poses with Rotation and Nonuniform Scale.

B.6 General Homography: The DLT Algorithm.

Nomenclature.

Bibliography.

Index.