Computer Vision and Action Recognition: A Guide for Image Processing and Computer Vision Community for Action Understanding
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.
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2 Lowlevel Image Processing for ActionRepresentations
3 Action Representation Approaches
4 MHI A Globalbased Generic Approach
5 Shape Representation and Feature VectorAnalysis
6 Action Datasets
7 Challenges Ahead
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Computer Vision and Action Recognition: A Guide for Image Processing and ...
Md. Atiqur Rahman Ahad
No preview available - 2014
action dataset action recognition activity AdaBoost algorithm Analysis and Machine applications approaches background subtraction behavior camera classifier cluster Conference on Computer database descriptors detection detector develop Discriminant Analysis DMHI dynamic employed Energy Image face recognition factorization method feature points feature vector filter Gabor gait analysis gait recognition Gesture Recognition Histogram Hu moments human action recognition IEEE Automatic Face IEEE Computer Vision IEEE Trans Image Processing image sequence interaction interest points International Conference invariant Linear Discriminant Analysis low-resolution Machine Intelligence Machine Vision matching measurement matrix MHI method motion capture Motion History Image occlusion optical flow outliers parameters Pattern Analysis Pattern Recognition performance pixel pose Principal Component Analysis problem propose RANSAC real-time recognize regions robot robust sample scene silhouette spatio-temporal streaklines Support Vector Machine templates temporal tion tracking trajectories Unsupervised learning various video sequences Vision and Pattern Wang