Human Activity Recognition and Prediction

1. Front Matter

   Pages i-vii

   PDF

2. Introduction

   • Yu Kong, Yun Fu

   Pages 1-22

3. Action Recognition and Human Interaction

   • Yu Kong, Yun Fu

   Pages 23-48

4. Subspace Learning for Action Recognition

   • Chengcheng Jia, Yun Fu

   Pages 49-69

5. Multimodal Action Recognition

   • Chengcheng Jia, Wei Pang, Yun Fu

   Pages 71-85

6. RGB-D Action Recognition

   • Chengcheng Jia, Yu Kong, Zhengming Ding, Yun Fu

   Pages 87-106

7. Activity Prediction

   • Yu Kong, Yun Fu

   Pages 107-122

8. Actionlets and Activity Prediction

   • Kang Li, Yun Fu

   Pages 123-151

9. Time Series Modeling for Activity Prediction

   • Kang Li, Sheng Li, Yun Fu

   Pages 153-174

This book provides a unique view of human activity recognition, especially fine-grained human activity structure learning, human-interaction recognition, RGB-D data based action recognition, temporal decomposition, and causality learning in unconstrained human activity videos. The techniques discussed give readers tools that provide a significant improvement over existing methodologies of video content understanding by taking advantage of activity recognition. It links multiple popular research fields in computer vision, machine learning, human-centered computing, human-computer interaction, image classification, and pattern recognition. In addition, the book includes several key chapters covering multiple emerging topics in the field. Contributed by top experts and practitioners, the chapters present key topics from different angles and blend both methodology and application, composing a solid overview of the human activity recognition techniques.

• Action Prediction
• Action Recognition
• Actionlets
• Activity Prediction
• Activity Recognition
• Computer Vision
• Discriminative Model
• Human Interaction
• Machine Learning
• Pattern Recognition
• RGB-D
• Transfer Learning

• Northeastern University, Boston, USA

  Yun Fu

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