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Analyzing Video Sequences of Multiple Humans: Tracking, Posture Estimation and Behavior Recognition [Kõva köide]

  • Formaat: Hardback, 138 pages, kõrgus x laius: 235x155 mm, kaal: 910 g, XXII, 138 p., 1 Hardback
  • Sari: The International Series in Video Computing 3
  • Ilmumisaeg: 31-Mar-2002
  • Kirjastus: Springer-Verlag New York Inc.
  • ISBN-10: 1402070217
  • ISBN-13: 9781402070211
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  • Formaat: Hardback, 138 pages, kõrgus x laius: 235x155 mm, kaal: 910 g, XXII, 138 p., 1 Hardback
  • Sari: The International Series in Video Computing 3
  • Ilmumisaeg: 31-Mar-2002
  • Kirjastus: Springer-Verlag New York Inc.
  • ISBN-10: 1402070217
  • ISBN-13: 9781402070211
Teised raamatud teemal:
This text describes some computer vision based methods that analyze methods for tracking multiple humans in a scene, estimating postures of a human body in 3D in real-time, and recognizing a person's gestures or activities. The five chapters detail the tracking algorithm developed by Ohya (Waseda U.), Utsumi (Advanced Telecommunications Research Institute) and Yamato (Nippon Telegraph and Telephone Corporation), which involves a non-synchronous method that exploits a Kalman filter applied to multiple video sequences. They also present the algorithm for estimating postures, and the method for recognizing human activities from a video sequence by the Hidden Markov Models. Appropriate for professional and academic researchers, as well as for use in graduate classes in computer vision or image processing. Annotation c. Book News, Inc., Portland, OR (booknews.com)

This book is an excellent reference for both professional and academic researchers in the fields of computer vision and image processing. It will also be of interest to those working in video surveillance and monitoring, virtual reality, computer graphics, pattern recognition, telecommunication, human-computer interface, and general computer science.Analyzing Video Sequences of Multiple Humans: Tracking, Posture Estimation and Behavior Recognition describes some computer vision-based methods that analyze video sequences of humans. More specifically, methods for tracking multiple humans in a scene, estimating postures of a human body in 3D in real-time, and recognizing a person's behavior (gestures or activities) are discussed. For the tracking algorithm, the authors developed a non-synchronous method that tracks multiple persons by exploiting a Kalman filter that is applied to multiple video sequences. For estimating postures, an algorithm is presented that locates the significant points which determine postures of a human body, in 3D in real-time. Human activities are recognized from a video sequence by the HMM (Hidden Markov Models)-based method that the authors pioneered. The effectiveness of the three methods is shown by experimental results.The posture estimation method described in this book is a world-leading method in that it can estimate postures in 3D in real-time. As described in this book, the posture estimation method can be applied to avatar-based telecommunication systems, which require realistic, real-time reproduction of human images.This book is also suitable for use in graduate classes in computer vision or image processing.

Muu info

Springer Book Archives
List of Figures
ix
List of Tables
xiii
Preface xvii
Contributing Authors xxi
Introduction
1(6)
Jun Ohya
Tracking multiple persons from multiple camera images
7(36)
Akira Utsumi
Overview
7(2)
Preparation
9(6)
Multiple Observations With Multiple Cameras (Observation Redundancy)
9(3)
Kalman Filtering
12(3)
Features of Multiple Camera Based Tracking System
15(2)
Algorithm for Multiple-Camera Human Tracking System
17(5)
Motion Tracking Of Multiple Targets
17(4)
Finding New Targets
21(1)
Implementation
22(4)
System Overview
22(1)
Feature Extraction
23(3)
Feature Matching at an Observation Node
26(1)
Experiments
26(7)
Discussion and Conclusions
33(10)
Appendix: Image Segmentation Using Sequential-Image-Based Adaptation
35(8)
Posture estimation
43(56)
Jun Ohya
Introduction
43(3)
A Heuristic Method for Estimating Postures in 2D
46(14)
Outline
46(1)
Locating significant points of the human body
46(1)
Center of Gravity of the Human Body
46(2)
Orientation of the Upper Half of the Human Body
48(1)
Locating Significant Points
49(3)
Estimating Major Joint Positions
52(1)
A GA Based Estimation Algorithm
52(1)
Elbow Joint Position
53(1)
Knee Joint Position
53(1)
Experimental Results and Discussions
54(1)
Experimental System
54(1)
Significant Point Location Results
54(1)
Joint Position Estimation
54(3)
Real-time Demonstration
57(1)
Summary
57(3)
A Heuristic Method for Estimating Postures in 3D
60(10)
Outline
60(1)
Image Processing for Top Camera
61(1)
Rotation Angle of the Body
61(2)
Significant Points
63(2)
Estimating Major Joint Positions
65(1)
3D Reconstruction of the Significant Points
66(1)
Experimental Results and Discussions
67(1)
Experimental System
67(1)
Significant Point Detection Results
67(2)
Summary
69(1)
A Non-heuristic Method for Estimating Postures in 3D
70(16)
Outline
70(1)
Locating Significant Points for Each Image
70(1)
Contour analysis
71(2)
The tracking process using Kalman filter and subtraction image processing
73(4)
3D Reconstruction of the Significant Points
77(1)
Front image
77(1)
Side image
77(1)
Top image
78(1)
Estimating 3D coordinates
79(1)
Experimental Results
79(1)
Experimental System
79(1)
Experimental Results
80(1)
Summary
81(5)
Applications to Virtual Environments
86(8)
Virtual Metamorphosis
86(1)
Virtual Kabuki System
87(5)
The ``Shall We Dance?'' system
92(2)
Discussion and Conclusion
94(5)
Recognizing human behavior using Hidden Markov Models
99(34)
Junji Yamato
Background and overview
99(3)
Hidden Markov Models
102(3)
Outline
102(2)
Recognition
104(1)
Learning
104(1)
Applying HMM to time-sequential images
105(3)
Experiments
108(6)
Experimental conditions and pre-processes
108(3)
Experiment 1
111(1)
Experimental conditions
111(1)
Results
111(1)
Experiment 2
111(1)
Experimental conditions
111(1)
Results
112(2)
Category-separated vector quantization
114(7)
Problem in VQ
114(1)
Category-separated VQ
114(1)
Experiment
114(7)
Applying Image Database Search
121(8)
Process overview
121(1)
Experiment 1: Evaluation of DCT
121(3)
Experiment 2: Evaluation of precision-recall
124(2)
Extracting a moving area using an MC vector
126(3)
Discussion and Conclusion
129(4)
Conclusion and Future Work
133(4)
Jun Ohya
Index 137