The Recognition of Human Movement Using Temporal Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real-time Human Motion Analysis by Image Skeletonization
WACV '98 Proceedings of the 4th IEEE Workshop on Applications of Computer Vision (WACV'98)
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Segmenting motion capture data into distinct behaviors
GI '04 Proceedings of the 2004 Graphics Interface Conference
3D Shape Context Based Gesture Analysis Integrated with Tracking using Omni Video Array
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Motion templates for automatic classification and retrieval of motion capture data
Proceedings of the 2006 ACM SIGGRAPH/Eurographics symposium on Computer animation
Human gait recognition based on matching of body components
Pattern Recognition
Behavior recognition via sparse spatio-temporal features
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
A 3-dimensional sift descriptor and its application to action recognition
Proceedings of the 15th international conference on Multimedia
A differential geometric approach to representing the human actions
Computer Vision and Image Understanding
View Invariant Human Action Recognition Based on Factorization and HMMs
IEICE - Transactions on Information and Systems
Efficient and robust annotation of motion capture data
Proceedings of the 2009 ACM SIGGRAPH/Eurographics Symposium on Computer Animation
Proceedings of the 2008 ACM SIGGRAPH/Eurographics Symposium on Computer Animation
Variable silhouette energy image representations for recognizing human actions
Image and Vision Computing
Discriminative human action recognition in the learned hierarchical manifold space
Image and Vision Computing
Human motion recognition based on hidden Markov models
ISICA'07 Proceedings of the 2nd international conference on Advances in computation and intelligence
Action Recognition in Videos Using Nonnegative Tensor Factorization
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Hierarchical Human Action Recognition by Normalized-Polar Histogram
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Human activity analysis: A review
ACM Computing Surveys (CSUR)
Learning dynamics for exemplar-based gesture recognition
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Real-time classification of dance gestures from skeleton animation
SCA '11 Proceedings of the 2011 ACM SIGGRAPH/Eurographics Symposium on Computer Animation
Recognition and segmentation of 3-d human action using HMM and multi-class adaboost
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Part-based motion descriptor image for human action recognition
Pattern Recognition
Real-time human pose recognition in parts from single depth images
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Human action recognition based on graph-embedded spatio-temporal subspace
Pattern Recognition
Activity Modeling Using Event Probability Sequences
IEEE Transactions on Image Processing
Mining actionlet ensemble for action recognition with depth cameras
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Human activity prediction: Early recognition of ongoing activities from streaming videos
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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Ongoing human action recognition is a challenging problem that has many applications, such as video surveillance, patient monitoring, human-computer interaction, etc. This paper presents a novel framework for recognizing streamed actions using Motion Capture (MoCap) data. Unlike the after-the-fact classification of completed activities, this work aims at achieving early recognition of ongoing activities. The proposed method is time efficient as it is based on histograms of action poses, extracted from MoCap data, that are computed according to Hausdorff distance. The histograms are then compared with the Bhattacharyya distance and warped by a dynamic time warping process to achieve their optimal alignment. This process, implemented by our dynamic programming-based solution, has the advantage of allowing some stretching flexibility to accommodate for possible action length changes. We have shown the success and effectiveness of our solution by testing it on large datasets and comparing it with several state-of-the-art methods. In particular, we were able to achieve excellent recognition rates that have outperformed many well known methods.