Example-Based Object Detection in Images by Components
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing Human Actions: A Local SVM Approach
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Semantic-level Understanding of Human Actions and Interactions using Event Hierarchy
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 1 - Volume 01
Human Action Recognition Using Multi-View Image Sequences Features
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Tracking People by Learning Their Appearance
IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of advances in vision-based human motion capture and analysis
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
HMM-Based Action Recognition Using Contour Histograms
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I
Recognizing human action from a far field of view
WMVC'09 Proceedings of the 2009 international conference on Motion and video computing
Survey on classifying human actions through visual sensors
Artificial Intelligence Review
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This paper presents a framework for view-invariant action recognition in image sequences. Feature-based human detection becomes extremely challenging when the agent is being observed from different viewpoints. Besides, similar actions, such as walking and jogging, are hardly distinguishable by considering the human body as a whole. In this work, we have developed a system which detects human body parts under different views and recognize similar actions by learning temporal changes of detected body part components. Firstly, human body part detection is achieved to find separately three components of the human body, namely the head, legs and arms. We incorporate a number of sub-classifiers, each for a specific range of view-point, to detect those body parts. Subsequently, we have extended this approach to distinguish and recognise actions like walking and jogging based on component-wise HMM learning.