VNBA '08 Proceedings of the 1st ACM workshop on Vision networks for behavior analysis
Linear-projection-based classification of human postures in time-of-flight data
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Human action recognition using boosted EigenActions
Image and Vision Computing
A survey on vision-based human action recognition
Image and Vision Computing
Appearance-based action recognition in the tensor framework
CIRA'09 Proceedings of the 8th IEEE international conference on Computational intelligence in robotics and automation
Learning an intrinsic-variable preserving manifold for dynamic visual tracking
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on game theory
Visual event recognition using decision trees
Multimedia Tools and Applications
Recognition of affect based on gait patterns
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on gait analysis
Approximate pairwise clustering for large data sets via sampling plus extension
Pattern Recognition
Comparing evaluation protocols on the KTH dataset
HBU'10 Proceedings of the First international conference on Human behavior understanding
Computer Vision and Image Understanding
Learning video manifold for segmenting crowd events and abnormality detection
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part I
HMM based semi-supervised learning for activity recognition
Proceedings of the 2011 international workshop on Situation activity & goal awareness
Human identification and gender recognition from boxing
CCBR'11 Proceedings of the 6th Chinese conference on Biometric recognition
Human action recognition using Pose-based discriminant embedding
Image Communication
Multi-view human movement recognition based on fuzzy distances and linear discriminant analysis
Computer Vision and Image Understanding
Spatiotemporal analysis of human activities for biometric authentication
Computer Vision and Image Understanding
Human action recognition using spatio-temporal classification
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
Human action recognition based on graph-embedded spatio-temporal subspace
Pattern Recognition
Spaces and manifolds of shapes in computer vision: An overview
Image and Vision Computing
Video manifold modelling: finding the right parameter settings for anomaly detection
Proceedings of the 27th Conference on Image and Vision Computing New Zealand
Human action recognition employing negative space features
Journal of Visual Communication and Image Representation
STV-based video feature processing for action recognition
Signal Processing
Fast action recognition using negative space features
Expert Systems with Applications: An International Journal
Common-sense reasoning for human action recognition
Pattern Recognition Letters
Shape classification by manifold learning in multiple observation spaces
Information Sciences: an International Journal
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In this paper, we learn explicit representations for dynamic shape manifolds of moving humans for the task of action recognition. We exploit locality preserving projections (LPP) for dimensionality reduction, leading to a low-dimensional embedding of human movements. Given a sequence of moving silhouettes associated to an action video, by LPP, we project them into a low-dimensional space to characterize the spatiotemporal property of the action, as well as to preserve much of the geometric structure. To match the embedded action trajectories, the median Hausdorff distance or normalized spatiotemporal correlation is used for similarity measures. Action classification is then achieved in a nearest-neighbor framework. To evaluate the proposed method, extensive experiments have been carried out on a recent dataset including ten actions performed by nine different subjects. The experimental results show that the proposed method is able to not only recognize human actions effectively, but also considerably tolerate some challenging conditions, e.g., partial occlusion, low-quality videos, changes in viewpoints, scales, and clothes; within-class variations caused by different subjects with different physical build; styles of motion; etc