Conception et mise en oeuvre d'algorithmes de vision temps-réel pour la vidéo surveillance intelligente.

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Ghorayeb, Hicham (2007) Conception et mise en oeuvre d'algorithmes de vision temps-réel pour la vidéo surveillance intelligente. Doctorat Informatique temps réel, robotique et automatique, CAOR- Centre de robotique, ENSMP p.197.

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Résumé

Notre objectif est d'étudier les algorithmes de vision utilisés aux différents niveaux dans une chaîne de traitement vidéo intelligente. On a prototypé une chaîne de traitement générique dédiée à l'analyse du contenu du flux vidéo. En se basant sur cette chaîne de traitement, on a développé une application de détection et de suivi de piétons. Cette application est une partie intégrante du projet PUVAME.

Cette chaîne de traitement générique est composée de plusieurs étapes: détection, classification et suivi d'objets. D'autres étapes de plus haut niveau sont envisagées comme la reconnaissance d'actions, l'identification, la description sémantique ainsi que la fusion des données de plusieurs caméras. On s'est intéressé aux deux premières étapes. On a exploré des algorithmes de segmentation du fond dans un flux vidéo avec caméra fixe. On a implémenté et comparé des algorithmes basés sur la modélisation adaptative du fond.

On a aussi exploré la détection visuelle d'objets basée sur l'apprentissage automatique en utilisant la technique du boosting. Cependant, On a développé une librairie intitulée LibAdaBoost qui servira comme un environnement de prototypage d'algorithmes d'apprentissage automatique. On a prototypé la technique du boosting au sein de cette librairie. On a distribué LibAdaBoost sous la licence LGPL. Cette librairie est unique avec les fonctionnalités qu'elle offre.

On a exploré l'utilisation des cartes graphiques pour l'accélération des algorithmes de vision. On a effectué le portage du détecteur visuel d'objets basé sur un classifieur généré par le boosting pour qu'il s'exécute sur le processeur graphique. On était les premiers à effectuer ce portage. On a trouvé que l'architecture du processeur graphique est la mieux adaptée pour ce genre d'algorithmes.

La chaîne de traitement a été implémentée et intégrée à l'environnement RTMaps. On a évalué ces algorithmes sur des scénarios bien définis. Ces scénarios ont été définis dans le cadre de PUVAME.

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Table des Matières

I Introduction and state of the art 1

1 French Introduction 2

1.1 Algorithmes - 4

1.2 Architecture - 4

1.3 Application - 5

2 Introduction 6

2.1 Contributions - 6

2.2 Outline - 7

3 Intelligent video surveillance systems (IVSS) 9

3.1 What is IVSS? - 10

3.1.1 Introduction - 10

3.1.2 Historical background - 10

3.2 Applications of intelligent surveillance - 11

3.2.1 Real time alarms - 11

3.2.2 User defined alarms - 11

3.2.3 Automatic unusual activity alarms - 12

3.2.4 Automatic Forensic Video Retrieval (AFVR) - 12

3.2.5 Situation awareness - 13

3.3 Scenarios and examples - 13

3.3.1 Public and commercial security - 13

3.3.2 Smart video data mining - 14

3.3.3 Law enforcement - 14

3.3.4 Military security - 14

3.4 Challenges - 14

3.4.1 Technical aspect - 14

3.4.2 Performance evaluation - 15

3.5 Choice of implementation methods - 15

3.6 Conclusion - 16



II Algorithms 17

4 Generic framework for intelligent visual surveillance 18

4.1 Object detection - 19

4.2 Object classification - 19

4.3 Object tracking - 20

4.4 Action recognition - 20

4.5 Semantic description - 21

4.6 Personal identification - 21

4.7 Fusion of data from multiple cameras - 21

5 Moving object detection 22

5.1 Challenge of detection - 23

5.2 Object detection system diagram - 25

5.2.1 Foreground detection - 26

5.2.2 Pixel level post-processing (Noise removal) - 27

5.2.3 Detecting connected components - 28

5.2.4 Region level post-processing - 28

5.2.5 Extracting object features - 28

5.3 Adaptive background differencing - 29

5.3.1 Basic Background Subtraction (BBS) - 29

5.3.2 W4 method - 30

5.3.3 Single Gaussian Model (SGM) - 31

5.3.4 Mixture Gaussian Model (MGM) - 32

5.3.5 Lehigh Omni-directional Tracking System (LOTS): - 33

5.4 Shadow and light change detection - 35

5.4.1 Methods and implementation - 36

5.5 High level feedback to improve detection methods - 46

5.5.1 The modular approach - 47

5.6 Performance evaluation - 48

5.6.1 Ground truth generation - 48

5.6.2 Datasets - 48

5.6.3 Evaluation metrics - 49

5.6.4 Experimental results - 54

5.6.5 Comments on the results - 56

6 Machine learning for visual object-detection 57

6.1 Introduction - 58

6.2 The theory of boosting - 58

6.2.1 Conventions and definitions - 58

6.2.2 Boosting algorithms - 60

6.2.3 AdaBoost - 61

6.2.4 Weak classifier - 63

6.2.5 Weak learner - 63



6.3 Visual domain - 66

6.3.1 Static detector - 67

6.3.2 Dynamic detector - 68

6.3.3 Weak classifiers - 68

6.3.4 Genetic weak learner interface - 75

6.3.5 Cascade of classifiers - 76

6.3.6 Visual finder - 77

6.4 LibAdaBoost: Library for Adaptive Boosting - 80

6.4.1 Introduction - 80

6.4.2 LibAdaBoost functional overview - 81

6.4.3 LibAdaBoost architectural overview - 85

6.4.4 LibAdaBoost content overview - 86

6.4.5 Comparison to previous work - 87

6.5 Use cases - 88

6.5.1 Car detection - 89

6.5.2 Face detection - 90

6.5.3 People detection - 91

6.6 Conclusion - 92

7 Object tracking 98

7.1 Initialization - 98

7.2 Sequential Monte Carlo tracking - 99

7.3 State dynamics - 100

7.4 Color distribution Model - 100

7.5 Results - 101

7.6 Incorporating Adaboost in the Tracker - 102

7.6.1 Experimental results - 102

III Architecture 107

8 General purpose computation on the GPU 108

8.1 Introduction - 108

8.2 Why GPGPU? - 109

8.2.1 Computational power - 109

8.2.2 Data bandwidth - 110

8.2.3 Cost/Performance ratio - 112

8.3 GPGPU’s first generation - 112

8.3.1 Overview - 112

8.3.2 Graphics pipeline - 114

8.3.3 Programming language - 119

8.3.4 Streaming model of computation - 120

8.3.5 Programmable graphics processor abstractions - 121

8.4 GPGPU’s second generation - 123



8.4.1 Programming model - 124

8.4.2 Application programming interface (API) - 124

8.5 Conclusion - 125

9 Mapping algorithms to GPU 126

9.1 Introduction - 126

9.2 Mapping visual object detection to GPU - 128

9.3 Hardware constraints - 130

9.4 Code generator - 131

9.5 Performance analysis - 133

9.5.1 Cascade Stages Face Detector (CSFD) - 133

9.5.2 Single Layer Face Detector (SLFD) - 134

9.6 Conclusion - 135

IV Application 137

10 Application: PUVAME 138

10.1 Introduction - 138

10.2 PUVAME overview - 139

10.3 Accident analysis and scenarios - 140

10.4 ParkNav platform - 141

10.4.1 The ParkView platform - 142

10.4.2 The CyCab vehicule - 144

10.5 Architecture of the system - 144

10.5.1 Interpretation of sensor data relative to the intersection - 145

10.5.2 Interpretation of sensor data relative to the vehicule - 149

10.5.3 Collision Risk Estimation - 149

10.5.4 Warning interface - 150

10.6 Experimental results - 151

V Conclusion and future work 153

11 Conclusion 154

11.1 Overview - 154

11.2 Future work - 155

12 French conclusion 157

VI Appendices 161

A Hello World GPGPU 162



B Hello World Brook 170

C Hello World CUDA 181

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