Analyzing Video Sequences of Multiple Humans
Tracking, Posture Estimation and Behavior Recognition
1st Edition
Published on 31. March 2002
Book
Hardback
XXII, 138 pages
978-1-4020-7021-1 (ISBN)
Description
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.
More details
Series
Language
English
Place of publication
New York
United States
Target group
Professional and scholarly
Professional/practitioner
Illustrations
XXII, 138 p.
Dimensions
Height: 23.5 cm
Width: 15.5 cm
Weight
910 gr
ISBN-13
978-1-4020-7021-1 (9781402070211)
DOI
10.1007/978-1-4615-1003-1
Schweitzer Classification
Other editions
Additional editions
Jun Ohya | Akira Utsumi | Junji Yamato
Analyzing Video Sequences of Multiple Humans
Tracking, Posture Estimation and Behavior Recognition
Book
10/2012
Springer
€160.49
Shipment within 7-9 days
Content
1 Introduction.- 2 Tracking multiple persons from multiple camera images.- 2.1 Overview.- 2.2 Preparation.- 2.4 Algorithm for Multiple-Camera Human Tracking System.- 2.5 Implementation.- 2.6 Experiments.- 2.7 Discussion and Conclusions.- Appendix: Image Segmentation using Sequential-image-based Adaptation.- 3 Posture estimation.- 3.1 Introduction.- 3.2 A Heuristic Method for Estimating Postures in 2D.- 3.3 A Heuristic Method for Estimating Postures in 3D.- 3.3.6 Summary.- 3.4 A Non-heuristic Method for Estimating Postures in 3D.- 3.5 Applications to Virtual Environments.- 3.6 Discussion and Conclusion.- 4 Recognizing human behavior using Hidden Markov Models.- 4.1 Background and overview.- 4.2 Hidden Markov Models.- 4.3 Applying HMM to time-sequential images.- 4.4 Experiments.- 4.5 Category-separated vector quantization.- 4.6 Applying Image Database Search.- 4.7 Discussion and Conclusion.- 5 Conclusion and Future Work.