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E-grāmata: Intelligent Video Surveillance Systems: An Algorithmic Approach

  • Formāts: 208 pages
  • Izdošanas datums: 27-Jun-2018
  • Izdevniecība: Chapman & Hall/CRC
  • Valoda: eng
  • ISBN-13: 9781498767125
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Intelligent Video Surveillance Systems: An Algorithmic ApproachPalielināt
  • Formāts: 208 pages
  • Izdošanas datums: 27-Jun-2018
  • Izdevniecība: Chapman & Hall/CRC
  • Valoda: eng
  • ISBN-13: 9781498767125
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This book will provide an overview of techniques for visual monitoring including video surveillance and human activity understanding. It will present the basic techniques of processing video from static cameras, starting with object detection and tracking. The author will introduce further video analytic modules including face detection, trajectory analysis and object classification. Examining system design and specific problems in visual surveillance, such as the use of multiple cameras and moving cameras, the author will elaborate on privacy issues focusing on approaches where automatic processing can help protect privacy.

List of Figures xv
List of Tables xix
Foreword xxi
Preface xxv
I Basics of Image and Video Processing 1(72)
1 Basics of Image Processing
3(34)
1.1 Introduction to Digital Image Processing
3(2)
1.1.1 Why Digital Image Processing?
3(1)
1.1.2 What Is Digital Image?
4(1)
1.1.3 What Is Digital Image Processing?
4(1)
1.2 Digital Image Processing System
5(3)
1.2.1 Image Acquisition
5(2)
1.2.2 Storage
7(1)
1.2.3 Processing
7(1)
1.2.4 Communication
7(1)
1.2.5 Display
7(1)
1.3 Digital Image Processing Methods
8(6)
1.3.1 Image Enhancement
8(1)
1.3.2 Image Restoration
8(1)
1.3.3 Image Segmentation
9(1)
1.3.4 Image Compression
9(1)
1.3.5 Image Reconstruction
10(1)
1.3.5.1 Analytical Reconstruction
10(1)
1.3.5.2 Iterative Reconstruction
11(1)
1.3.6 Image Morphing
11(1)
1.3.7 Image Recognition
12(1)
1.3.8 Image Mosaicing
12(1)
1.3.9 Image Watermarking
13(1)
1.3.10 Image Registration
14(1)
1.4 Digital Image Segmentation
14(18)
1.4.1 Classification of Image Segmentation Techniques
15(1)
1.4.2 Edge Detection
15(8)
1.4.2.1 Classification of Edges
16(1)
1.4.2.2 Gradient Operator
16(2)
1.4.2.3 Laplacian Operator
18(1)
1.4.2.4 Marr Hildreth Edge Detector
19(2)
1.4.2.5 Isolated Point Detection
21(1)
1.4.2.6 Line Detection
21(1)
1.4.2.7 Canny Edge Detector
21(2)
1.4.3 Edge Linking
23(4)
1.4.3.1 Local Processing
24(1)
1.4.3.2 Regional Processing
25(1)
1.4.3.3 Global Processing Using Hough Transform
25(2)
1.4.4 Thresholding
27(3)
1.4.4.1 Multiple Thresholding
27(1)
1.4.4.2 Global Thresholding
28(2)
1.4.4.3 Local Thresholding
30(1)
1.4.5 Region Growing
30(1)
1.4.6 Region Splitting and Merging
30(1)
1.4.7 Watershed-Based Segmentation
31(1)
1.4.7.1 Use of Markers
32(1)
1.5 Applications
32(4)
1.5.1 Television Signal Processing
32(1)
1.5.2 Satellite Image Processing
33(1)
1.5.3 Medical Image Processing
34(1)
1.5.4 Robot Control
34(1)
1.5.5 Visual Communications
35(1)
1.5.6 Law Enforcement
35(1)
1.6 Summary
36(1)
2 Basics of Video Compression and Motion Analysis
37(22)
2.1 Video Compression
37(9)
2.1.1 What Is Video Compression?
37(1)
2.1.2 Why Video Compression?
37(1)
2.1.3 Types of Video Compression
38(1)
2.1.3.1 Lossless
38(1)
2.1.3.2 Lossy
39(1)
2.1.4 Latency
39(1)
2.1.5 MPEG Compression
40(5)
2.1.5.1 Reduction of the Resolution
40(1)
2.1.5.2 Motion Estimation
41(3)
2.1.5.3 Discrete Cosine Transform
44(1)
2.1.5.4 Quantization
44(1)
2.1.5.5 Entropy Coding
44(1)
2.1.6 Video Compression Standards
45(1)
2.2 Motion Segmentation
46(4)
2.2.1 Introduction
46(2)
2.2.1.1 Issues in Motion Segmentations
46(1)
2.2.1.2 Main Attributes of a Motion Segmentation Algorithm
47(1)
2.2.2 Motion Segmentation Algorithms
48(2)
2.2.2.1 Image Difference
48(1)
2.2.2.2 Statistical Theory
48(1)
2.2.2.3 Optical Flow
49(1)
2.2,2.4 Layers
49(1)
2.2.2.5 Factorization Technique
50(1)
2.3 Optical Flow Methods
50(6)
2.3.1 Estimation of Optical Flow
50(6)
2.3.1.1 Horn-Schunck Optical Flow Estimation
51(2)
2.3.1.2 Lucas Kanade Optical Flow Estimation
53(3)
2.4 Applications
56(2)
2.4.1 Surveillance and Security
56(1)
2.4.2 Content-Based Video Indexing and Retrieval
56(1)
2.4.3 Automatic Highlight Generation of Sports Videos
57(1)
2.4.4 Traffic Monitoring
57(1)
2.5 Summary
58(1)
3 Background Modeling
59(14)
3.1 What Is Background Modeling?
59(1)
3.2 Background Modeling Techniques
59(8)
3.2.1 Non-Statistical Background Modeling Methods
61(2)
3.2.1.1 Background Modeling Independent of Time
61(1)
3.2.1.2 Improved Basic Background Modeling
61(1)
3.2.1.3 Long-Term Average Background Modeling
62(1)
3.2.2 Statistical Background Modeling Methods
63(4)
3.2.2.1 Example of GMM
63(1)
3.2.2.2 GMM Model
63(2)
3.2.2.3 Expectation Maximization GMM Algorithm
65(1)
3.2.2.4 GMM-Based Background Detection
66(1)
3.3 Shadow Detection and Removal
67(4)
3.3.1 Shadow Removal for Traffic Flow Detection
69(2)
3.4 Summary
71(2)
II Object Tracking 73(66)
4 Object Classification
75(16)
4.1 Shape-Based Object Classification
76(1)
4.2 Motion-Based Object Classification
76(1)
4.2.1 Approaches
76(1)
4.2.2 Applications
77(1)
4.3 Viola Jones Object Detection Framework
77(4)
4.3.1 Haar Features
78(1)
4.3.2 Integral Image
79(1)
4.3.3 AdaBoost Training
80(1)
4.3.4 Cascading of Classifiers
81(1)
4.3.5 Results and Discussion
81(1)
4.4 Object Classification Using Convolutional Neural Networks
81(7)
4.4.1 What Are Convolutional Neural Networks?
82(3)
4.4.1.1 Convolution Stage
83(1)
4.4.1.2 Non-Linear Activation Stage
83(1)
4.4.1.3 Pooling Stage
84(1)
4.4.2 Convolutional Neural Network Models
85(1)
4.4.2.1 Two-Layer Convolutional Neural Network
85(1)
4.4.2.2 Three-Layer Convolutional Neural Network
86(1)
4.4.2.3 Intuition for Using Deep Neural Networks
86(1)
4.4.3 Results and Discussion
86(2)
4.4.3.1 Experimental Datasets
86(1)
4.4.3.2 Results and Discussion
87(1)
4.5 Object Classification Using Regional Convolutional Neural Net- works
88(1)
4.5.1 Steps of RCNN Algorithm
88(1)
4.5.2 Results and Discussion
88(1)
4.6 Summary
89(2)
5 Human Activity Recognition
91(24)
5.1 Motion History Image-Based Human Activity Recognition
91(7)
5.1.1 Motion History Image
91(2)
5.1.2 Hu Moments
93(1)
5.1.2.1 Hu's Invariant Moments
94(1)
5.1.3 Human Activity Recognition
94(4)
5.1.3.1 Classification Using Hu Moments
96(1)
5.1.3.2 Projection and Displacement Features
96(1)
5.1.3.3 Experimental Discussion
97(1)
5.2 Hidden Markov Models
98(5)
5.2.1 Markov Models
98(1)
5.2.2 Hidden Markov Models
99(1)
5.2.2.1 Final State
100(1)
5.2.3 The Three Fundamental Problems of HMM
100(3)
5.2.3.1 Likelihood Evaluation
101(1)
5.2.3.2 State Sequence Decoding
101(2)
5.2.3.3 HMM Parameter Estimation
103(1)
5.2.4 Limitations of Hidden Markov Models
103(1)
5.3 HMM-Based Activity Recognition
103(7)
5.3.1 Shape-Based Features
104(2)
5.3.1.1 Discrete Fourier Transform
105(1)
5.3.1.2 Principal Component Analysis
105(1)
5.3.1.3 K means Clustering
105(1)
5.3.2 Optical Flow-Based Features
106(3)
5.3.2.1 Lucas Kanade Optical Flow Method
106(1)
5.3.2.2 Optical Features
107(2)
5.3.3 Implementation and Results
109(1)
5.4 Dynamic Time Warping-Based Activity Recognition
110(2)
5.4.1 What Is Dynamic Time Warping?
110(2)
5.4.2 Implementation
112(1)
5.5 Abnormal Activity Recognition
112(2)
5.6 Challenges of Intelligent Human Activity Recognition
114(1)
5.7 Summary
114(1)
6 Video Object Tracking
115(24)
6.1 Introduction
115(3)
6.1.1 What Is Video Object Tracking?
115(1)
6.1.2 Tracking Challenges
115(1)
6.1.3 Steps of Video Object Tracking System
116(2)
6.1.3.1 Background Identification
117(1)
6.1.3.2 Foreground Object Detection
117(1)
6.1.3.3 Object Labeling
118(1)
6.1.3.4 Handling the Occlusion Problem
118(1)
6.2 Kalman Filter
118(6)
6.2.1 What Is a Kalman Filter?
118(1)
6.2.2 How Does a Kalman Filter Work?
119(1)
6.2.3 Kalman Filter Cycle
119(1)
6.2.4 Basic Theory of Kalman Filter
120(3)
6.2.4.1 Prediction Equations
120(1)
6.2.4.2 Update Equations
121(1)
6.2.4.3 Measurement Equations
122(1)
6.2.5 Implementation
123(1)
6.3 Region-Based Tracking
124(1)
6.4 Contour-Based Tracking
125(1)
6.5 Feature-Based Tracking
126(2)
6.5.1 Feature-Based Tracking Algorithm
126(4)
6.5.1.1 Feature Selection
126(1)
6.5.1.2 Sum-of-Squared-Difference Criterion
127(1)
6.5.1.3 Pyramidal Decomposition
127(1)
6.6 Model-Based Tracking
128(2)
6.7 KLT Tracker
130(3)
6.7.1 Limitations of the KLT Tracker
132(1)
6.8 Mean-Shift-Based Tracking
133(2)
6.8.1 What Is Mean Shift?
133(1)
6.8.2 Algorithm
133(1)
6.8.3 Advantages
134(1)
6.8.4 Disadvantages
134(1)
6.9 Applications of Tracking Algorithms
135(3)
6.9.1 Trajectory-Based Unusual Human Movement Recognition
135(6)
6.9.1.1 Closed Path Detection
136(1)
6.9.1.2 Spiral Path Detection
136(2)
6.10 Summary
138(1)
III Surveillance Systems 139(34)
7 Camera Network for Surveillance
141(16)
7.1 Types of CCTV Cameras
141(4)
7.1.1 Bullet Camera
141(1)
7.1.2 Dome Camera
141(1)
7.1.3 Desktop Cameras
142(1)
7.1.4 Vari-Focal Camera
142(1)
7.1.5 IP Camera
143(1)
7.1.6 Wireless Camera
143(1)
7.1.7 PTZ Camera
143(1)
7.1.8 Infrared Cameras
144(1)
7.1.9 Day Night Cameras
145(1)
7.1.10 High-Definition Cameras
145(1)
7.2 Smart Cameras
145(2)
7.2.1 What Is a Smart Camera?
145(1)
7.2.2 Components of Smart Cameras
146(1)
7.2.3 Why Do We Need a Smart Camera?
146(1)
7.2.4 Features
146(1)
7.3 Smart Imagers
147(1)
7.3.1 What Is an Imager?
147(1)
7.3.2 Types of Imagers
147(2)
7.3.2.1 CCD Imager
147(1)
7.3.2.2 Complementary Metal Oxide Semiconductor
148(1)
7.4 Multiple View Geometry
148(1)
7.5 Camera Network
149(1)
7.5.1 What Is Camera Networking?
149(1)
7.5.2 Camera Devices
149(1)
7.6 Camera Calibration
150(2)
7.7 Camera Placement
152(1)
7.7.1 Camera Placement Algorithm for Region with Obstacles
152(1)
7.8 Camera Communication
153(1)
7.9 Multiple Camera Coordination and Cooperation
154(1)
7.10 Summary
155(2)
8 Surveillance Systems and Applications
157(16)
8.1 Introduction
157(1)
8.1.1 Components of Video Surveillance Systems
157(1)
8.2 Video Content Analytics
158(2)
8.2.1 Functionalities
158(1)
8.2.2 Commercial Applications
158(1)
8.2.3 Video Object Tracking
159(1)
8.3 Baggage Exchange Detection
160(5)
8.3.1 Object Detection Using GMM
160(3)
8.3.1.1 Background Subtraction
161(1)
8.3.1.2 Gaussian Mixture Model
162(1)
8.3.2 Tracking Using a Kalman Filter
163(1)
8.3.3 Labelling of Different Objects
163(1)
8.3.4 Identification of Baggage and Person
163(1)
8.3.5 Waning System in Case of Exchange
164(1)
8.3.6 Results
164(1)
8.4 Fence-Crossing Detection
165(3)
8.4.1 Proposed Fence-Crossing Detection System
165(2)
8.4.1.1 Foreground Extraction
165(1)
8.4.1.2 Feature Extraction and Selection
166(1)
8.4.1.3 SVM Classifier
166(1)
8.4.2 Experimental Results
167(1)
8.5 Military Applications
168(2)
8.5.1 The Need for Automation
168(1)
8.5.2 Basic Design of Surveillance Systems
169(1)
8.6 Transportation
170(2)
8.6.1 Road Transportation
170(1)
8.6.2 Rail Transportation
170(1)
8.6.3 Maritime Transportation
171(1)
8.6.3.1 Challenges in Maritime Transportation
171(1)
8.7 Summary
172(1)
Bibliography 173(6)
Index 179
Dr Maheshkumar H Kolekar is working as Assistant Professor in Electrical Engineering Indian Institute of Technology Patna He also served as Coordinator of Electrical Engineering.
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