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Automated Video Surveillance |
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1 | (10) |
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1 | (1) |
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Automated Systems for Video Surveillance |
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2 | (2) |
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Automated Surveillance System Tasks and Related Technical Challenges |
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4 | (2) |
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Object Detection and Categorization |
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4 | (1) |
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4 | (1) |
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5 | (1) |
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6 | (1) |
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Introduction to the Proposed Video Understanding Algorithms for Surveillance |
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6 | (3) |
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9 | (2) |
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Identifying Regions of Interest In Image Sequences |
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11 | (18) |
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11 | (1) |
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General Problems in Background Subtraction |
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12 | (1) |
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13 | (4) |
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Background Subtraction using Color as a Feature |
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14 | (2) |
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Background Subtraction using Multiple Features |
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16 | (1) |
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Finite State Space Based Background Subtraction |
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17 | (1) |
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Proposed Approach for Background Subtraction |
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17 | (5) |
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18 | (1) |
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18 | (3) |
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21 | (1) |
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22 | (1) |
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22 | (2) |
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24 | (5) |
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Object Detection And Categorization |
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29 | (16) |
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29 | (1) |
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Problems in Object Categorization |
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30 | (1) |
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30 | (3) |
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Periodicity Based Categorization |
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30 | (1) |
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Object Categorization using Supervised Classifiers |
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31 | (1) |
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Object Categorization using Weakly Supervised Classifiers |
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32 | (1) |
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Overview of the proposed categorization approach |
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33 | (1) |
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Feature Selection and Base Classifiers |
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34 | (2) |
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The Co-Training Framework |
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36 | (3) |
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37 | (2) |
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Co-Training Ability Measurement |
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39 | (1) |
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39 | (3) |
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42 | (3) |
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Object Tracking In A Single Camera |
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45 | (14) |
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45 | (1) |
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45 | (4) |
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Feature Point Tracking Methods |
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46 | (1) |
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47 | (1) |
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48 | (1) |
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Problems in Tracking 2D silhouettes of People |
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49 | (1) |
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50 | (1) |
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50 | (1) |
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Proposed Approach for Tracking |
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50 | (2) |
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50 | (1) |
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51 | (1) |
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52 | (2) |
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54 | (5) |
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Tracking In Multiple Cameras With Disjoint Views |
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59 | (26) |
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Problem Overview and Key Challenges |
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59 | (2) |
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61 | (3) |
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Multi-Camera Tracking Methods Requiring Overlapping Views: |
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61 | (1) |
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Multi-Camera Tracking Methods for Non-Overlapping Views: |
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62 | (2) |
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Formulation of the Multi-Camera Tracking Problem |
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64 | (2) |
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Learning Inter-Camera Space-Time Probabilities |
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66 | (1) |
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Estimating Change in Appearances across Cameras |
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67 | (5) |
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The Space of Brightness Transfer Functions |
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68 | (3) |
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Estimation of Inter-Camera BTFs and their Subspace |
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71 | (1) |
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Computing Object Color Similarity Across Cameras Using the BTF Subspace |
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72 | (1) |
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Establishing Correspondences |
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72 | (2) |
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74 | (7) |
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81 | (4) |
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Knight: Surveillance System Deployment |
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85 | (6) |
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85 | (1) |
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Deploying Surveillance Systems: Ethical Considerations |
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85 | (1) |
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86 | (3) |
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89 | (2) |
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91 | (4) |
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91 | (1) |
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91 | (1) |
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Understanding Complex Human Interaction & Activities |
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92 | (1) |
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The Properties of a Good Surveillance System and How Knight Measures Up |
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92 | (3) |
References |
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95 | (8) |
Index |
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103 | |