Computer Vision and Image Understanding
Understanding of human behaviors from videos in nursing care monitoring systems
Journal of High Speed Networks - Broadband Multimedia Sensor Networks in Healthcare Applications
An architecture for a self configurable video supervision
AREA '08 Proceedings of the 1st ACM workshop on Analysis and retrieval of events/actions and workflows in video streams
Human Activity Recognition Using the 4D Spatiotemporal Shape Context Descriptor
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part II
View-invariant human activity recognition based on shape and motion features
International Journal of Robotics and Automation
Variable silhouette energy image representations for recognizing human actions
Image and Vision Computing
Wearable sensor activity analysis using semi-Markov models with a grammar
Pervasive and Mobile Computing
Independent shape component-based human activity recognition via Hidden Markov Model
Applied Intelligence
Action recognition with global features
ICCV'05 Proceedings of the 2005 international conference on Computer Vision in Human-Computer Interaction
Extracting motion features for visual human activity representation
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part I
Incremental behavior modeling and suspicious activity detection
Pattern Recognition
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This paper deals with the problem of classification of human activities from video as one way of performing activity monitoring. Our approach uses motion features that are computed very efficiently and subsequently projected into a lower dimension space where matching is performed. Each action is represented as a manifold in this lower dimension space and matching is done by comparing these manifolds. To demonstrate the effectiveness of this approach, it was used on a large data set of similar actions, each performed by many different actors. Classification results are accurate and show that this approach can handle many challenges such as variations in performers' physical attributes, color of clothing, and style of motion. An important result of this paper is that the recovery of three-dimensional properties of a moving person or even two-dimensional tracking of the person's limbs are not necessary steps that must precede action recognition.