Human motion analysis: a review
Computer Vision and Image Understanding
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
The Recognition of Human Movement Using Temporal Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Color-spatial image indexing and applications
Color-spatial image indexing and applications
Recognizing Action at a Distance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
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
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
The Pyramid Match Kernel: Discriminative Classification with Sets of Image Features
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Human Action Recognition Using LBP-TOP as Sparse Spatio-Temporal Feature Descriptor
CAIP '09 Proceedings of the 13th International Conference on Computer Analysis of Images and Patterns
Behavior histograms for action recognition and human detection
Proceedings of the 2nd conference on Human motion: understanding, modeling, capture and animation
Recent advances and trends in visual tracking: A review
Neurocomputing
Human action segmentation and recognition via motion and shape analysis
Pattern Recognition Letters
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The performance of human motion classification and recognition systems is highly dependent on the distinctiveness and robustness of the feature descriptor. In this paper, a new descriptor containing motion, shape and spatial layout information is proposed, therefore it is more effective for action modeling and is suitable for detecting and recognizing a variety of actions. Experiments show that the proposed descriptor outperforms other existing methods, such as Moment Invariants and Histogram of Oriented Gradients, on recognizing human motions in an indoor environment with a stationary camera.