A survey on vision-based human action recognition
Image and Vision Computing
Relative Margin Support Tensor Machines for gait and action recognition
Proceedings of the ACM International Conference on Image and Video Retrieval
Action recognition based on human movement characteristics
WMVC'09 Proceedings of the 2009 international conference on Motion and video computing
A survey of multilinear subspace learning for tensor data
Pattern Recognition
Correlation-based retrieval for heavily changed near-duplicate videos
ACM Transactions on Information Systems (TOIS)
Multiway canonical correlation analysis for frequency components recognition in SSVEP-Based BCIs
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part I
Higher rank Support Tensor Machines for visual recognition
Pattern Recognition
Directional space-time oriented gradients for 3d visual pattern analysis
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Action recognition using canonical correlation kernels
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
Human gesture recognition on product manifolds
The Journal of Machine Learning Research
Application of 3D-wavelet statistics to video analysis
Multimedia Tools and Applications
Kernel analysis on Grassmann manifolds for action recognition
Pattern Recognition Letters
A template matching approach of one-shot-learning gesture recognition
Pattern Recognition Letters
Learning canonical correlations of paired tensor sets via tensor-to-vector projection
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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This paper addresses a spatiotemporal pattern recognition problem. The main purpose of this study is to find a right representation and matching of action video volumes for categorization. A novel method is proposed to measure video-to-video volume similarity by extending Canonical Correlation Analysis (CCA), a principled tool to inspect linear relations between two sets of vectors, to that of two multiway data arrays (or tensors). The proposed method analyzes video volumes as inputs avoiding the difficult problem of explicit motion estimation required in traditional methods and provides a way of spatiotemporal pattern matching that is robust to intraclass variations of actions. The proposed matching is demonstrated for action classification by a simple Nearest Neighbor classifier. We, moreover, propose an automatic action detection method, which performs 3D window search over an input video with action exemplars. The search is speeded up by dynamic learning of subspaces in the proposed CCA. Experiments on a public action data set (KTH) and a self-recorded hand gesture data showed that the proposed method is significantly better than various state-of-the-art methods with respect to accuracy. Our method has low time complexity and does not require any major tuning parameters.