Analyzing video sequences of multiple humans : tracking, posture estimation and behavior recognition
Author(s)
Bibliographic Information
Analyzing video sequences of multiple humans : tracking, posture estimation and behavior recognition
（The Kluwer international series in video computing）
Kluwer Academic, c2002
Available at / 13 libraries
-
No Libraries matched.
- Remove all filters.
Note
Includes bibliographical references(p.130-131) and index
Description and Table of Contents
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.
Table of Contents
- List of Figures. List of Tables. Preface. Contributing Authors. 1: Introduction
- J. Ohya. 2: Tracking multiple persons from multiple camera images
- A. Utsumi. 2.1. Overview. 2.2. Preparation. 2.3. Features of Multiple camera based tracking systems. 2.4. Algorithms for multiple-camera human tracking system. 2.5. Implementation. 2.6. Experiments. 2.7. Discussion and Conclusions 3: Posture estimation
- J. Ohya. 3.1. Introduction. 3.2. A heuristic for estimating postures in 2D. 3.3. A heuristic method for estimating postures in 3D. 3.4. A non-heuristic method for estimating postures in 3D. 3.5. Applications to virtual environments. 3.6. Discussions and conclusions. 4: Recognizing human behavior using Hidden Markov Models
- J. Yamato. 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. Discussions and conclusion. 5: Conclusion and Future Work
- J. Ohya. Index.
by "Nielsen BookData"
