| Preface | 5 |
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| Contents | 8 |
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| List of Figures | 13 |
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| List of Tables | 21 |
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| Introduction to Gait-Based Individual Recognition at a Distance | 24 |
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| Introduction | 25 |
| Gait-Based Human Recognition | 26 |
| Face-Based Human Recognition | 26 |
| Key Ideas Described in the Book | 27 |
| Organization of the Book | 29 |
| Gait-Based Individual Recognition at a Distance | 32 |
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| Gait Representations in Video | 33 |
| Human Motion Analysis and Representations | 33 |
| Human Activity and Individual Recognition by Gait | 34 |
| Human Recognition by Gait | 35 |
| Model-Based Approaches | 35 |
| Model-Free Approaches | 35 |
| Human Activity Recognition | 37 |
| Model-Based Approaches | 37 |
| Model-Free Approaches | 37 |
| Gait Energy Image (GEI) Representation | 37 |
| Motivation | 38 |
| Representation Construction | 38 |
| Relationship with MEI and MHI | 38 |
| Representation Justification | 39 |
| Framework for GEI-Based Recognition | 41 |
| Silhouette Extraction and Processing | 41 |
| Feature Extraction | 42 |
| Summary | 44 |
| Model-Free Gait-Based Human Recognition in Video | 45 |
| Statistical Feature Fusion for Human Recognition by Gait | 45 |
| Real and Synthetic Gait Templates | 46 |
| Human Recognition | 48 |
| Experimental Results | 50 |
| Data and Parameters | 50 |
| Performance Evaluation | 52 |
| Human Recognition Based on Environmental Context | 53 |
| Walking Surface Type Detection | 54 |
| Classifier Design | 57 |
| Probabilistic Classifier Combination | 58 |
| Experimental Results | 59 |
| View-Insensitive Human Recognition by Gait | 60 |
| View-Insensitive Gait Templates | 60 |
| Human Recognition | 62 |
| Experimental Results | 63 |
| Human Repetitive Activity Recognition in Thermal Imagery | 65 |
| Object Detection in Thermal Infrared Imagery | 66 |
| Human Repetitive Activity Representation and Recognition | 67 |
| Experimental Results | 68 |
| Human Recognition Under Different Carrying Conditions | 70 |
| Technical Approach | 70 |
| Gait Energy Image (GEI) | 70 |
| Feature Extraction | 71 |
| Co-evolutionary Genetic Programming | 72 |
| Majority Voting | 73 |
| Experimental Results | 73 |
| Data | 73 |
| Experiments | 74 |
| Classifier Performance Comparison | 74 |
| Summary | 75 |
| Discrimination Analysis for Model-Based Gait Recognition | 77 |
| Predicting Human Recognition Performance | 77 |
| Algorithm Dependent Prediction and Performance Bounds | 78 |
| Body Part Length Distribution | 78 |
| Algorithm Dependent Performance Prediction | 80 |
| Upper Bound on PCR | 81 |
| Experimental Results | 82 |
| Summary | 83 |
| Model-Based Human Recognition-2D and 3D Gait | 85 |
| 2D Gait Recognition (3D Model, 2D Data) | 85 |
| 3D Human Modeling | 86 |
| Human Kinematic Model | 86 |
| Human Model Parameter Selection | 87 |
| Camera Model and Coordinate Transformation | 88 |
| World Coordinate to Camera Coordinate | 89 |
| Camera Coordinate to Ideal Image Coordinate | 89 |
| Ideal Image Coordinate to Actual Image Coordinate | 89 |
| Actual Image Coordinate to Computer Image Coordinate | 90 |
| Human Recognition from Single Non-calibrated Camera | 90 |
| Silhouette Preprocessing | 90 |
| Matching Between 3D Model and 2D Silhouette | 91 |
| Human Model Parameter Estimation | 91 |
| Stationary Parameter Estimation | 91 |