The Recognition of Human Movement Using Temporal Templates
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
International Journal of Computer Vision
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Successive Convex Matching for Action Detection
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Conditional models for contextual human motion recognition
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
A 3-dimensional sift descriptor and its application to action recognition
Proceedings of the 15th international conference on Multimedia
Hidden Conditional Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Searching for Complex Human Activities with No Visual Examples
International Journal of Computer Vision
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Action categorization with modified hidden conditional random field
Pattern Recognition
Sparsity preserving projections with applications to face recognition
Pattern Recognition
Human Action Recognition by Semilatent Topic Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Human Action Recognition in Videos Using Kinematic Features and Multiple Instance Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Activities as time series of human postures
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Action Recognition from One Example
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sparse regularization for semi-supervised classification
Pattern Recognition
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
-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
IEEE Transactions on Signal Processing
Sparse Representation for Color Image Restoration
IEEE Transactions on Image Processing
Action recognition using linear dynamic systems
Pattern Recognition
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
Learning group-based dictionaries for discriminative image representation
Pattern Recognition
Hi-index | 0.01 |
In this paper, we propose a new supervised classification method based on a modified sparse model for action recognition. The main contributions are three-fold. First, a novel hierarchical descriptor is presented for action representation. To capture spatial information about neighboring interest points, a compound motion and appearance feature is proposed for the interest point at low level. Furthermore, at high level, a continuous motion segment descriptor is presented to combine temporal ordering information of motion. Second, we propose a modified sparse model which incorporates the similarity constrained term and the dictionary incoherence term for classification. Our sparse model not only captures the correlations between similar samples by sharing dictionary, but also encourages dictionaries associated with different classes to be independent by the dictionary incoherence term. The proposed sparse model targets classification, rather than pure reconstruction. Third, in the sparse model, we adopt a specific dictionary for each action class. Moreover, a classification loss function is proposed to optimize the class-specific dictionaries. Experiments validate that the proposed framework obtains the performance comparable to the state-of-the-art.