Analyzing Video Sequences of Multiple Humans

1. Front Matter

   Pages i-xxii

   PDF

2. Introduction

   • Jun Ohya

   Pages 1-5

3. Tracking Multiple Persons from Multiple Camera Images

   • Akira Utsumi

   Pages 7-42

4. Posture Estimation

   • Jun Ohya

   Pages 43-98

5. Recognizing Human Behavior Using Hidden Markov Models

   • Junji Yamato

   Pages 99-131

6. Conclusion and Future Work

   • Jun Ohya

   Pages 133-135

7. Back Matter

   Pages 137-138

   PDF

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.

• 3D
• Virtual Environments
• Virtual Reality
• algorithms
• computer graphics
• computer vision
• graphics
• image processing
• pattern recognition
• video

• Waseda University, Japan

  Jun Ohya

• Advanced Telecommunications Research Institute International, Japan

  Akira Utsumi

• Nippon Telegraph & Telephone Corporation, Japan

  Junji Yamato

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