Model-based object pose in 25 lines of code
International Journal of Computer Vision - Special issue: image understanding research at the University of Maryland
The visual analysis of human movement: a survey
Computer Vision and Image Understanding
The Recognition of Human Movement Using Temporal Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
Real-Time Context-Based Gesture Recognition Using HMM and Automaton
RATFG-RTS '99 Proceedings of the International Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems
Hidden Markov Model Based Continuous Online Gesture Recognition
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Space-Time Behavior Based Correlation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
International Journal of Computer Vision
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
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
Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words
International Journal of Computer Vision
Video Metrology Using a Single Camera
IEEE Transactions on Pattern Analysis and Machine Intelligence
Part-based motion descriptor image for human action recognition
Pattern Recognition
Human activity recognition using multi-features and multiple kernel learning
Pattern Recognition
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This paper presents a real-time or online system for continuous recognition of human actions. The system recognizes actions such as walking, bending, jumping, waving, and falling and relies on spatial features computed to characterize human posture. The paper evaluates the utility of these features based on its joint or independent treatment within the context of the Hidden Markov Model (HMM) framework. A baseline approach wherein disparate spatial features are treated as an input vector to trained HMMs is used to compare three different independent feature models. In addition, an action transition constraints is introduced to stabilize the developed models and allow for continuity in recognized actions. The system is evaluated across a dataset of videos and results reported in terms of frame error rate, the frame delay in recognizing an action, action recognition rate, and the missed and false recognition rates. Experimental results shows the effectiveness of the proposed treatment of input features and the corresponding HMM formulations.