Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing Human Actions: A Local SVM Approach
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
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
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
SIAM Journal on Imaging Sciences
A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
SIAM Journal on Imaging Sciences
Two-frame motion estimation based on polynomial expansion
SCIA'03 Proceedings of the 13th Scandinavian conference on Image analysis
Object, scene and actions: combining multiple features for human action recognition
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Modeling temporal structure of decomposable motion segments for activity classification
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Convolutional learning of spatio-temporal features
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Action Recognition Using Mined Hierarchical Compound Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sparse reconstruction cost for abnormal event detection
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Recognizing human actions by attributes
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Action recognition by dense trajectories
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Learning Sparse Representations for Human Action Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning spatiotemporal graphs of human activities
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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Recognizing actions in a video is a critical step for making many vision-based applications possible and has attracted much attention recently. However, action recognition in a video is a challenging task due to wide variations within an action, camera motion, cluttered background, and occlusions, to name a few. While dense sampling based approaches are currently achieving the state-of-the-art performance in action recognition, they do not perform well for many realistic video sequences since, by considering every motion found in a video equally, the discriminative power of these approaches is often reduced due to clutter motions, such as background changes and camera motions. In this paper, we robustly identify local motions of interest in an unsupervised manner by taking advantage of group sparsity. In order to robustly classify action types, we emphasize local motion by combining local motion descriptors and full motion descriptors and apply group sparsity to the emphasized motion features using the multiple kernel method. In experiments, we show that different types of actions can be well recognized using a small number of selected local motion descriptors and the proposed algorithm achieves the state-of-the-art performance on popular benchmark datasets, outperforming existing methods. We also demonstrate that the group sparse representation with the multiple kernel method can dramatically improve the action recognition performance.