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Model-based Visual Tracking: The OpenTL Framework [Kõva köide]

  • Formaat: Hardback, 318 pages, kõrgus x laius x paksus: 245x164x22 mm, kaal: 638 g, Illustrations
  • Ilmumisaeg: 10-May-2011
  • Kirjastus: Wiley-Blackwell
  • ISBN-10: 0470876131
  • ISBN-13: 9780470876138
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Model-based Visual Tracking: The OpenTL FrameworkSuurem pilt
  • Formaat: Hardback, 318 pages, kõrgus x laius x paksus: 245x164x22 mm, kaal: 638 g, Illustrations
  • Ilmumisaeg: 10-May-2011
  • Kirjastus: Wiley-Blackwell
  • ISBN-10: 0470876131
  • ISBN-13: 9780470876138
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780470876138.html
Märksõnad:
This book has two main goals: to provide a unifed and structured overview of this growing field, as well as to propose a corresponding software framework, the OpenTL library, developed by the author and his working group at TUM-Informatik. The main objective of this work is to show, how most real-world application scenarios can be naturally cast into a common description vocabulary, and therefore implemented and tested in a fully modular and scalable way, through the defnition of a layered, object-oriented software architecture.The resulting architecture covers in a seamless way all processing levels, from raw data acquisition up to model-based object detection and sequential localization, and defines, at the application level, what we call the tracking pipeline. Within this framework, extensive use of graphics hardware (GPU computing) as well as distributed processing, allows real-time performances for complex models and sensory systems.
Preface xi
1 Introduction
1(11)
1.1 Overview of the Problem
2(4)
1.1.1 Models
3(2)
1.1.2 Visual Processing
5(1)
1.1.3 Tracking
6(1)
1.2 General Tracking System Prototype
6(2)
1.3 The Tracking Pipeline
8(4)
2 Model Representation
12(43)
2.1 Camera Model
13(13)
2.1.1 Internal Camera Model
13(3)
2.1.2 Nonlinear Distortion
16(1)
2.1.3 External Camera Parameters
17(1)
2.1.4 Uncalibrated Models
18(2)
2.1.5 Camera Calibration
20(6)
2.2 Object Model
26(13)
2.2.1 Shape Model and Pose Parameters
26(8)
2.2.2 Appearance Model
34(3)
2.2.3 Learning an Active Shape or Appearance Model
37(2)
2.3 Mapping Between Object and Sensor Spaces
39(4)
2.3.1 Forward Projection
40(1)
2.3.2 Back-Projection
41(2)
2.4 Object Dynamics
43(12)
2.4.1 Brownian Motion
47(2)
2.4.2 Constant Velocity
49(1)
2.4.3 Oscillatory Model
49(1)
2.4.4 State Updating Rules
50(2)
2.4.5 Learning AR Models
52(3)
3 The Visual Modality Abstraction
55(23)
3.1 Preprocessing
55(2)
3.2 Sampling and Updating Reference Features
57(2)
3.3 Model Matching with the Image Data
59(11)
3.3.1 Pixel-Level Measurements
62(2)
3.3.2 Feature-Level Measurements
64(3)
3.3.3 Object-Level Measurements
67(1)
3.3.4 Handling Mutual Occlusions
68(2)
3.3.5 Multiresolution Processing for Improving Robustness
70(1)
3.4 Data Fusion Across Multiple Modalities and Cameras
70(8)
3.4.1 Multimodal Fusion
71(1)
3.4.2 Multicamera Fusion
71(1)
3.4.3 Static and Dynamic Measurement Fusion
72(5)
3.4.4 Building a Visual Processing Tree
77(1)
4 Examples Of Visual Modalities
78(84)
4.1 Color Statistics
79(14)
4.1.1 Color Spaces
80(5)
4.1.2 Representing Color Distributions
85(4)
4.1.3 Model-Based Color Matching
89(1)
4.1.4 Kernel-Based Segmentation and Tracking
90(3)
4.2 Background Subtraction
93(3)
4.3 Blobs
96(16)
4.3.1 Shape Descriptors
97(7)
4.3.2 Blob Matching Using Variational Approaches
104(8)
4.4 Model Contours
112(14)
4.4.1 Intensity Edges
114(5)
4.4.2 Contour Lines
119(3)
4.4.3 Local Color Statistics
122(4)
4.5 Keypoints
126(14)
4.5.1 Wide-Baseline Matching
128(1)
4.5.2 Harris Corners
129(4)
4.5.3 Scale-Invariant Keypoints
133(5)
4.5.4 Matching Strategies for Invariant Keypoints
138(2)
4.6 Motion
140(7)
4.6.1 Motion History Images
140(2)
4.6.2 Optical Flow
142(5)
4.7 Templates
147(15)
4.7.1 Pose Estimation with AAM
151(7)
4.7.2 Pose Estimation with Mutual Information
158(4)
5 Recursive State-Space Estimation
162(35)
5.1 Target-State Distribution
163(3)
5.2 MLE and MAP Estimation
166(6)
5.2.1 Least-Squares Estimation
167(1)
5.2.2 Robust Least-Squares Estimation
168(4)
5.3 Gaussian Filters
172(8)
5.3.1 Kalman and Information Filters
172(1)
5.3.2 Extended Kalman and Information Filters
173(3)
5.3.3 Unscented Kalman and Information Filters
176(4)
5.4 Monte Carlo Filters
180(12)
5.4.1 SIR Particle Filter
181(4)
5.4.2 Partitioned Sampling
185(2)
5.4.3 Annealed Particle Filter
187(2)
5.4.4 MCMC Particle Filter
189(3)
5.5 Grid Filters
192(5)
6 Examples Of Target Detectors
197(17)
6.1 Blob Clustering
198(4)
6.1.1 Localization with Three-Dimensional Triangulation
199(3)
6.2 AdaBoost Classifiers
202(2)
6.2.1 AdaBoost Algorithm for Object Detection
202(1)
6.2.2 Example: Face Detection
203(1)
6.3 Geometric Hashing
204(4)
6.4 Monte Carlo Sampling
208(3)
6.5 Invariant Keypoints
211(3)
7 Building Applications With Opentl
214(37)
7.1 Functional Architecture of OpenTL
214(2)
7.1.1 Multithreading Capabilities
216(1)
7.2 Building a Tutorial Application with OpenTL
216(24)
7.2.1 Setting the Camera Input and Video Output
217(3)
7.2.2 Pose Representation and Model Projection
220(4)
7.2.3 Shape and Appearance Model
224(3)
7.2.4 Setting the Color-Based Likelihood
227(5)
7.2.5 Setting the Particle Filter and Tracking the Object
232(3)
7.2.6 Tracking Multiple Targets
235(2)
7.2.7 Multimodal Measurement Fusion
237(3)
7.3 Other Application Examples
240(11)
APPENDIX A POSE ESTIMATION
251(14)
A.1 Point Correspondences
251(8)
A.1.1 Geometric Error
253(1)
A.1.2 Algebraic Error
253(1)
A.1.3 2D-2D and 3D-3D Transforms
254(2)
A.1.4 DLT Approach for 3D-2D Projections
256(3)
A.2 Line Correspondences
259(2)
A.2.1 2D-2D Line Correspondences
260(1)
A.3 Point and Line Correspondences
261(1)
A.4 Computation of the Projective DLT Matrices
262(3)
APPENDIX B POSE REPRESENTATION
265(16)
B.1 Poses Without Rotation
265(3)
B.1.1 Pure Translation
266(1)
B.1.2 Translation and Uniform Scale
267(1)
B.1.3 Translation and Nonuniform Scale
267(1)
B.2 Parameterizing Rotations
268(4)
B.3 Poses with Rotation and Uniform Scale
272(3)
B.3.1 Similarity
272(1)
B.3.2 Rotation and Uniform Scale
273(1)
B.3.3 Euclidean (Rigid Body) Transform
274(1)
B.3.4 Pure Rotation
274(1)
B.4 Affinity
275(2)
B.5 Poses with Rotation and Nonuniform Scale
277(1)
B.6 General Homography: The DLT Algorithm
278(3)
Nomenclature 281(4)
Bibliography 285(10)
Index 295
Giorgio Panin, PhD , is an Assistant Professor at the Technical University of Munich. His research focuses on robotics and embedded systems, with an emphasis on computer vision and object tracking for human-machine interaction, robot manipulation, and navigation tasks.