Human action analyses and recognition are challenging problems due to large variations in human motion and appearance, camera viewpoint and environment settings. The field of action and activity representation and recognition is relatively old, yet not well-understood by the students and research community. Some important but common motion recognition problems are even now unsolved properly by the computer vision community. However, in the last decade, a number of good approaches are proposed and evaluated subsequently by many researchers. Among those methods, some methods get significant attention from many researchers in the computer vision field due to their better robustness and performance. This book will cover gap of information and materials on comprehensive outlook through various strategies from the scratch to the state-of-the-art on computer vision regarding action recognition approaches. This book will target the students and researchers who have knowledge on image processing at a basic level and would like to explore more on this area and do research. The step by step methodologies will encourage one to move forward for a comprehensive knowledge on computer vision for recognizing various human actions.

No other book is available that covers action recognition in a comprehensive manner in the arena of computer vision Both students, who have basic knowledge on image processing, and researchers can use this book for their academic learning and research purposes This book can be used as a text book for level-2 students who have studied basic image processing already in earlier session both for undergraduate and graduate students The book will cover all state-of-the-art methods on recognition approaches and guide one on how to move forward in the field Includes supplementary material: sn.pub/extras

Inhalt

Introduction.- Low-level Image Processing for Action Representations.- Action Representation Approaches.- MHI A Global-based Generic Approach.- Shape Representation and Feature Vector Analysis .- Action Datasets.-Challenges Ahead.