Recognizing Action at a Distance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Multiple kernel learning, conic duality, and the SMO algorithm
ICML '04 Proceedings of the twenty-first international conference on Machine learning
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
Actions Sketch: A Novel Action Representation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Exploring the Space of a Human Action
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Efficient Visual Event Detection Using Volumetric Features
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Large Scale Multiple Kernel Learning
The Journal of Machine Learning Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words
International Journal of Computer Vision
Scale Invariant Action Recognition Using Compound Features Mined from Dense Spatio-temporal Corners
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Spatial-Temporal correlatons for unsupervised action classification
WMVC '08 Proceedings of the 2008 IEEE Workshop on Motion and video Computing
Efficient human action detection using a transferable distance function
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
Learning and Matching of Dynamic Shape Manifolds for Human Action Recognition
IEEE Transactions on Image Processing
Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing
Human action recognition employing negative space features
Journal of Visual Communication and Image Representation
Fast action recognition using negative space features
Expert Systems with Applications: An International Journal
Kernel analysis on Grassmann manifolds for action recognition
Pattern Recognition Letters
Human activity recognition using multi-features and multiple kernel learning
Pattern Recognition
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Most of the existing action recognition methods represent actions as bags of space-time interest points. Specifically, space-time interest points are detected from the video and described using appearance-based descriptors. Each descriptor is then classified as a video-word and a histogram of these video-words is used for recognition. These methods therefore rely solely on the discriminative power of individual local space-time descriptors, whilst ignoring the potentially useful information about the global spatio-temporal distribution of interest points. In this paper we propose a novel action representation method which differs significantly from the existing interest point based representation in that only the global distribution information of interest points is exploited. In particular, holistic features from clouds of interest points accumulated over multiple temporal scales are extracted. Since the proposed spatio-temporal distribution representation contains different but complementary information to the conventional Bag of Words representation, we formulate a feature fusion method based on Multiple Kernel Learning. Experiments using the KTH and WEIZMANN datasets demonstrate that our approach outperforms most existing methods, in particular under occlusion and changes in view angle, clothing, and carrying condition.