Free viewpoint action recognition using motion history volumes
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
View-invariant action recognition using interest points
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Unsupervised view and rate invariant clustering of video sequences
Computer Vision and Image Understanding
Rate-invariant recognition of humans and their activities
IEEE Transactions on Image Processing
A survey on vision-based human action recognition
Image and Vision Computing
Volumetric Features for Video Event Detection
International Journal of Computer Vision
Advances in view-invariant human motion analysis: a review
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
View and style-independent action manifolds for human activity recognition
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Human action recognition using a dynamic Bayesian action network with 2D part models
Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing
Accurate person tracking through changing poses for multi-view action recognition
Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing
A survey of vision-based methods for action representation, segmentation and recognition
Computer Vision and Image Understanding
Gait analysis of gender and age using a large-scale multi-view gait database
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part II
An unsupervised framework for action recognition using actemes
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part IV
Fast human activity recognition based on structure and motion
Pattern Recognition Letters
Multi-view human movement recognition based on fuzzy distances and linear discriminant analysis
Computer Vision and Image Understanding
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We present a new method for segmenting actions into primitives and classifying them into a hierarchy of action classes. Our scheme learns action classes in an unsupervised manner using examples recorded by multiple cameras. Segmentation and clustering of action classes is based on a recently proposed motion descriptor which can be extracted efficiently from reconstructed volume sequences. Because our representation is independent of viewpoint, it results in segmentation and classification methods which are surprisingly efficient and robust. Our new method can be used as the first step in a semi-supervised action recognition system that will automatically break down training examples of people performing sequences of actions into primitive actions that can be discriminatingly classified and assembled into high-level recognizers.