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 field. Contributed by top experts and practitioners, the chapters complement each other from different angles and compose a solid overview of the human activity recognition techniques. Well-balanced content for both methodology and real-world applications will
benefit readers from different levels of expertise.
Introduction.- Action and Activities.- Action Recognition and Human Interaction.- Multimodal Action Recognition.- RGB-D Action Recognition.- Actionlets and Activity Prediction.- Time Series Modeling for Activity Prediction.- RGB-D Action Prediction.
