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E-raamat: Intelligent Video Surveillance Systems

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  • Ilmumisaeg: 14-Dec-2012
  • Kirjastus: ISTE Ltd and John Wiley & Sons Inc
  • Keel: eng
  • ISBN-13: 9781118577936
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Intelligent Video Surveillance SystemsSuurem pilt
  • Formaat: EPUB+DRM
  • Ilmumisaeg: 14-Dec-2012
  • Kirjastus: ISTE Ltd and John Wiley & Sons Inc
  • Keel: eng
  • ISBN-13: 9781118577936
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Belonging to the wider academic field of computer vision, video analytics has aroused a phenomenal surge of interest since the current millennium. Video analytics is intended to solve the problem of the incapability of exploiting video streams in real time for the purpose of detection or anticipation. It involves analyzing the videos using algorithms that detect and track objects of interest over time and that indicate the presence of events or suspect behavior involving these objects.

The aims of this book are to highlight the operational attempts of video analytics, to identify possible driving forces behind potential evolutions in years to come, and above all to present the state of the art and the technological hurdles which have yet to be overcome. The need for video surveillance is introduced through two major applications (the security of rail transportation systems and a posteriori investigation). The characteristics of the videos considered are presented through the cameras which enable capture and the compression methods which allow us to transport and store them. Technical topics are then discussed the analysis of objects of interest (detection, tracking and recognition), "high-level" video analysis, which aims to give a semantic interpretation of the observed scene (events, behaviors, types of content). The book concludes with the problem of performance evaluation.
Introduction xiii
Jean-Yves Dufour
Phlippe Mouttou
Chapter 1 Image Processing: Overview and Perspectives
1(12)
Henri Maitre
1.1 Half a century ago
1(2)
1.2 The use of images
3(1)
1.3 Strengths and weaknesses of image processing
4(2)
1.3.1 What are these theoretical problems that image processing has been unable to overcome?
5(1)
1.3.2 What are the problems that image processing has overcome?
5(1)
1.4 What is left for the future?
6(3)
1.5 Bibliography
9(4)
Chapter 2 Focus on Railway Transport
13(20)
Sebastien Ambellouis
Jean-Luc Bruyelle
2.1 Introduction
13(2)
2.2 Surveillance of railway infrastructures
15(6)
2.2.1 Needs analysis
15(1)
2.2.2 Which architectures?
16(1)
2.2.3 Detection and analysis of complex events
17(3)
2.2.4 Surveillance of outside infrastructures
20(1)
2.3 Onboard surveillance
21(7)
2.3.1 Surveillance of buses
22(1)
2.3.2 Applications to railway transport
23(5)
2.4 Conclusion
28(2)
2.5 Bibliography
30(3)
Chapter 3 A Posteriori Analysis for Investigative Purposes
33(14)
Denis Marraud
Benjamin Cepas
Jean-Francis Sulzer
Christianne Mulat
Florence Sedes
3.1 Introduction
33(1)
3.2 Requirements in tools for assisted investigation
34(2)
3.2.1 Prevention and security
34(1)
3.2.2 Information gathering
35(1)
3.2.3 Inquiry
36(1)
3.3 Collection and storage of data
36(3)
3.3.1 Requirements in terms of standardization
37(1)
3.3.2 Attempts at standardization (AFNOR and ISO)
37(2)
3.4 Exploitation of the data
39(5)
3.4.1 Content-based indexing
39(4)
3.4.2 Assisted investigation tools
43(1)
3.5 Conclusion
44(1)
3.6 Bibliography
45(2)
Chapter 4 Video Surveillance Cameras
47(18)
Cedric Le Barz
Thierry Lamarque
4.1 Introduction
47(1)
4.2 Constraints
48(1)
4.2.1 Financial constraints
48(1)
4.2.2 Environmental constraints
49(1)
4.3 Nature of the information captured
49(4)
4.3.1 Spectral bands
50(1)
4.3.2 3D or "2D + Z" imaging
51(2)
4.4 Video formats
53(2)
4.5 Technologies
55(2)
4.6 Interfaces: from analog to IP
57(4)
4.6.1 From analog to digital
57(2)
4.6.2 The advent of IP
59(1)
4.6.3 Standards
60(1)
4.7 Smart cameras
61(1)
4.8 Conclusion
62(1)
4.9 Bibliography
63(2)
Chapter 5 Video Compression Formats
65(22)
Marc Leny
Didier Nicholson
5.1 Introduction
65(1)
5.2 Video formats
66(4)
5.2.1 Analog video signals
66(1)
5.2.2 Digital video: standard definition
67(1)
5.2.3 High definition
68(1)
5.2.4 The CIF group of formats
69(1)
5.3 Principles of video compression
70(4)
5.3.1 Spatial redundancy
70(3)
5.3.2 Temporal redundancy
73(1)
5.4 Compression standards
74(9)
5.4.1 MPEG-2
74(1)
5.4.2 MPEG-4 Part 2
75(2)
5.4.3 MPEG-4 Part 10/H.264 AVC
77(2)
5.4.4 MPEG-4 Part 10/H.264 SVC
79(1)
5.4.5 Motion JPEG 2000
80(2)
5.4.6 Summary of the formats used in video surveillance
82(1)
5.5 Conclusion
83(1)
5.6 Bibliography
84(3)
Chapter 6 Compressed Domain Analysis for Fast Activity Detection
87(16)
Marc Leny
6.1 Introduction
87(1)
6.2 Processing methods
88(5)
6.2.1 Use of transformed coefficients in the frequency domain
88(2)
6.2.2 Use of motion estimation
90(1)
6.2.3 Hybrid approaches
91(2)
6.3 Uses of analysis of the compressed domain
93(7)
6.3.1 General architecture
94(2)
6.3.2 Functions for which compressed domain analysis is reliable
96(1)
6.3.3 Limitations
97(3)
6.4 Conclusion
100(1)
6.5 Acronyms
101(1)
6.6 Bibliography
101(2)
Chapter 7 Detection of Objects of Interest
103(20)
Yoann Dhome
Bertrand Luvison
Thierry Chesnais
Rachid Belaroussi
Laurent Lucat
Mohamed Chaouch
Patrick Sayd
7.1 Introduction
103(1)
7.2 Moving object detection
104(5)
7.2.1 Object detection using background modeling
104(3)
7.2.2 Motion-based detection of objects of interest
107(2)
7.3 Detection by modeling of the objects of interest
109(8)
7.3.1 Detection by geometric modeling
109(2)
7.3.2 Detection by visual modeling
111(6)
7.4 Conclusion
117(1)
7.5 Bibliography
118(5)
Chapter 8 Tracking of Objects of Interest in a Sequence of Images
123(24)
Simona Maggio
Jean-Emmanuel Haugeard
Boris Meden
Bertrand Luvison
Romaric Audigier
Brice Burger
Quoc Cuong Pham
8.1 Introduction
123(1)
8.2 Representation of objects of interest and their associated visual features
124(3)
8.2.1 Geometry
124(1)
8.2.2 Characteristics of appearance
125(2)
8.3 Geometric workspaces
127(1)
8.4 Object-tracking algorithms
127(5)
8.4.1 Deterministic approaches
127(1)
8.4.2 Probabilistic approaches
128(4)
8.5 Updating of the appearance models
132(3)
8.6 Multi-target tracking
135(3)
8.6.1 MHT and JPDAF
135(1)
8.6.2 MCMC and RJMCMC sampling techniques
136(2)
8.6.3 Interactive filters, track graph
138(1)
8.7 Object tracking using a PTZ camera
138(3)
8.7.1 Object tracking using a single PTZ camera only
139(1)
8.7.2 Object tracking using a PTZ camera coupled with a static camera
139(2)
8.8 Conclusion
141(1)
8.9 Bibliography
142(5)
Chapter 9 Tracking Objects of Interest Through a Camera Network
147(18)
Catherine Achard
Sebastien Ambellouis
Boris Meden
Sebastien Lefebvre
Dung Nghi
Truong Cong
9.1 Introduction
147(1)
9.2 Tracking in a network of cameras whose fields of view overlap
148(7)
9.2.1 Introduction and applications
148(2)
9.2.2 Calibration and synchronization of a camera network
150(3)
9.2.3 Description of the scene by multi-camera aggregation
153(2)
9.3 Tracking through a network of cameras with non-overlapping fields of view
155(6)
9.3.1 Issues and applications
155(1)
9.3.2 Geometric and/or photometric calibration of a camera network
156(1)
9.3.3 Reidentification of objects of interest in a camera network
157(3)
9.3.4 Activity recognition/event detection in a camera network
160(1)
9.4 Conclusion
161(1)
9.5 Bibliography
161(4)
Chapter 10 Biometric Techniques Applied to Video Surveillance
165(18)
Bernadette Dorizzi
Samuel Vinson
10.1 Introduction
165(1)
10.2 The databases used for evaluation
166(2)
10.2.1 NIST-Multiple Biometrics Grand Challenge (NIST-MBGC)
167(1)
10.2.2 Databases of faces
167(1)
10.3 Facial recognition
168(5)
10.3.1 Face detection
168(1)
10.3.2 Face recognition in biometrics
169(1)
10.3.3 Application to video surveillance
170(3)
10.4 Iris recognition
173(4)
10.4.1 Methods developed for biometrics
173(1)
10.4.2 Application to video surveillance
174(2)
10.4.3 Systems for iris capture in videos
176(1)
10.4.4 Summary and perspectives
177(1)
10.5 Research projects
177(1)
10.6 Conclusion
178(1)
10.7 Bibliography
179(4)
Chapter 11 Vehicle Recognition in Video Surveillance
183(18)
Stephane Herbin
11.1 Introduction
183(1)
11.2 Specificity of the context
184(1)
11.2.1 Particular objects
184(1)
11.2.2 Complex integrated chains
185(1)
11.3 Vehicle modeling
185(4)
11.3.1 Wire models
186(1)
11.3.2 Global textured models
187(1)
11.3.3 Structured models
188(1)
11.4 Exploitation of object models
189(5)
11.4.1 A conventional sequential chain with limited performance
189(1)
11.4.2 Improving shape extraction
190(1)
11.4.3 Inferring 3D information
191(1)
11.4.4 Recognition without form extraction
192(1)
11.4.5 Toward a finer description of vehicles
193(1)
11.5 Increasing observability
194(2)
11.5.1 Moving observer
194(1)
11.5.2 Multiple observers
195(1)
11.6 Performances
196(1)
11.7 Conclusion
196(1)
11.8 Bibliography
197(4)
Chapter 12 Activity Recognition
201(18)
Bernard Boulay
Francis Bremond
12.1 Introduction
201(1)
12.2 State of the art
202(4)
12.2.1 Levels of abstraction
202(1)
12.2.2 Modeling and recognition of activities
203(3)
12.2.3 Overview of the state of the art
206(1)
12.3 Ontology
206(4)
12.3.1 Objects of interest
207(1)
12.3.2 Scenario models
208(1)
12.3.3 Operators
209(1)
12.3.4 Summary
210(1)
12.4 Suggested approach: the ScReK system
210(2)
12.5 Illustrations
212(3)
12.5.1 Application at an airport
213(1)
12.5.2 Modeling the behavior of elderly people
213(2)
12.6 Conclusion
215(1)
12.7 Bibliography
215(4)
Chapter 13 Unsupervised Methods for Activity Analysis and Detection of Abnormal Events
219(16)
Remi Emonet
Jean-Marc Odobez
13.1 Introduction
219(2)
13.2 An example of a topic model: PLSA
221(5)
13.2.1 Introduction
221(1)
13.2.2 The PLSA model
221(2)
13.2.3 PLSA applied to videos
223(3)
13.3 PLSM and temporal models
226(4)
13.3.1 PLSM model
226(2)
13.3.2 Motifs extracted by PLSM
228(2)
13.4 Applications: counting, anomaly detection
230(3)
13.4.1 Counting
230(1)
13.4.2 Anomaly detection
230(1)
13.4.3 Sensor selection
231(2)
13.4.4 Prediction and statistics
233(1)
13.5 Conclusion
233(1)
13.6 Bibliography
233(2)
Chapter 14 Data Mining in a Video Database
235(16)
Luis Patino
Hamid Benhadda
Francis Bremond
14.1 Introduction
235(1)
14.2 State of the art
236(1)
14.3 Pre-processing of the data
237(1)
14.4 Activity analysis and automatic classification
238(7)
14.4.1 Unsupervised learning of zones of activity
239(3)
14.4.2 Definition of behaviors
242(1)
14.4.3 Relational analysis
243(2)
14.5 Results and evaluations
245(3)
14.6 Conclusion
248(1)
14.7 Bibliography
249(2)
Chapter 15 Analysis of Crowded Scenes in Video
251(22)
Mikel Rodriguez
Josef Sivic
Ivan Laptev
15.1 Introduction
251(2)
15.2 Literature review
253(4)
15.2.1 Crowd motion modeling and segmentation
253(1)
15.2.2 Estimating density of people in a crowded scene
254(1)
15.2.3 Crowd event modeling and recognition
255(1)
15.2.4 Detecting and tracking in a crowded scene
256(1)
15.3 Data-driven crowd analysis in videos
257(5)
15.3.1 Off-line analysis of crowd video database
258(1)
15.3.2 Matching
258(2)
15.3.3 Transferring learned crowd behaviors
260(1)
15.3.4 Experiments and results
260(2)
15.4 Density-aware person detection and tracking in crowds
262(6)
15.4.1 Crowd model
263(1)
15.4.2 Tracking detections
264(1)
15.4.3 Evaluation
265(3)
15.5 Conclusions and directions for future research
268(1)
15.6 Acknowledgments
268(1)
15.7 Bibliography
269(4)
Chapter 16 Detection of Visual Context
273(16)
Herve Le Borgne
Aymen Shabou
16.1 Introduction
273(2)
16.2 State of the art of visual context detection
275(4)
16.2.1 Overview
275(1)
16.2.2 Visual description
276(2)
16.2.3 Multiclass learning
278(1)
16.3 Fast shared boosting
279(2)
16.4 Experiments
281(4)
16.4.1 Detection of boats in the Panama Canal
281(2)
16.4.2 Detection of the visual context in video surveillance
283(2)
16.5 Conclusion
285(1)
16.6 Bibliography
286(3)
Chapter 17 Example of an Operational Evaluation Platform: PPSL
289(8)
Stephane Braudel
17.1 Introduction
289(1)
17.2 Use of video surveillance: approach and findings
290(2)
17.3 Current use contexts and new operational concepts
292(1)
17.4 Requirements in smart video processing
293(1)
17.5 Conclusion
294(3)
Chapter 18 Qualification and Evaluation of Performances
297(18)
Bernard Boulay
Jean-Francois Goudou
Francois Bremond
18.1 Introduction
297(1)
18.2 State of the art
298(5)
18.2.1 Applications
298(1)
18.2.2 Process
299(4)
18.3 An evaluation program: ETISEO
303(6)
18.3.1 Methodology
303(2)
18.3.2 Metrics
305(2)
18.3.3 Summary
307(2)
18.4 Toward a more generic evaluation
309(3)
18.4.1 Contrast
310(2)
18.4.2 Shadows
312(1)
18.5 The Quasper project
312(1)
18.6 Conclusion
313(1)
18.7 Bibliography
314(1)
List of Authors 315(6)
Index 321
Jean-Yves Dufour is Head of the Vision Lab at Thales/CEA common research laboratory. Since early 2009, he has been running the video analytics joint laboratory of the innovation office of the Thales DSC division (Defense and Security C4I Systems). This laboratory is set up in partnership with the CEA-LIST (one of the top three academic research centers in France). In parallel to his professional activities, he teaches image processing at ISEN, Lille, France.
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