Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Recognition of Human Movement Using Temporal Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contour and Texture Analysis for Image Segmentation
International Journal of Computer Vision
Multiclass Spectral Clustering
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Recognizing Human Actions: A Local SVM Approach
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Probabilistic Kernels for the Classification of Auto-Regressive Visual Processes
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
International Journal of Computer Vision
Successive Convex Matching for Action Detection
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
A survey of advances in vision-based human motion capture and analysis
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words
International Journal of Computer Vision
Contour graph based human tracking and action sequence recognition
Pattern Recognition
A survey on vision-based human action recognition
Image and Vision Computing
Modeling temporal structure of decomposable motion segments for activity classification
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Activities as time series of human postures
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Shift-Invariant dynamic texture recognition
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Local descriptors for spatio-temporal recognition
SCVMA'04 Proceedings of the First international conference on Spatial Coherence for Visual Motion Analysis
Activity recognition using dynamic subspace angles
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Machine Recognition of Human Activities: A Survey
IEEE Transactions on Circuits and Systems for Video Technology
Sparse dictionary-based representation and recognition of action attributes
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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In this paper, we propose a novel approach based on Linear Dynamic Systems (LDSs) for action recognition. Our main contributions are two-fold. First, we introduce LDSs to action recognition. LDSs describe the dynamic texture which exhibits certain stationarity properties in time. They are adopted to model the spatiotemporal patches which are extracted from the video sequence, because the spatiotemporal patch is more analogous to a linear time invariant system than the video sequence. Notably, LDSs do not live in the Euclidean space. So we adopt the kernel principal angle to measure the similarity between LDSs, and then the multiclass spectral clustering is used to generate the codebook for the bag of features representation. Second, we propose a supervised codebook pruning method to preserve the discriminative visual words and suppress the noise in each action class. The visual words which maximize the inter-class distance and minimize the intra-class distance are selected for classification. Our approach yields the state-of-the-art performance on three benchmark datasets. Especially, the experiments on the challenging UCF Sports and Feature Films datasets demonstrate the effectiveness of the proposed approach in realistic complex scenarios.