Recognizing Action at a Distance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Recognizing Human Actions: A Local SVM Approach
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Space-Time Behavior Based Correlation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Activity Recognition and Abnormality Detection with the Switching Hidden Semi-Markov Model
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
Conditional Random Fields for Contextual Human Motion Recognition
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Conditional Random People: Tracking Humans with CRFs and Grid Filters
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Behavior recognition via sparse spatio-temporal features
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
k-means++: the advantages of careful seeding
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
Online, Real-time Tracking and Recognition of Human Actions
WMVC '08 Proceedings of the 2008 IEEE Workshop on Motion and video Computing
Learning dynamics for exemplar-based gesture recognition
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
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Graphical models have been shown to provide a natural framework for modelling high level action transition constraints, and to simultaneously segment and recognize a sequence of actions. More recently, Spatio-temporal Interest Points (STIPs) have been proposed as suitable features for action detection. These interest points are typically mapped to a set of codewords, and actions are detected by accumulating the codeword weights or by learning suitable classifiers. Existing methods for interest point detection provide a sparse representation of actions and require a costly exhaustive search over the entire spatio-temporal volume for action classification. Our contribution here is two-fold - first, we combine the interest point models of actions with pedestrian detection and tracking using a Conditional Random Field (CRF); second, we extend existing interest point detectors to provide a dense action representation while minimizing spurious detections. The larger number of interest points and the high-level reasoning provided by the CRF allows us to automatically recognize action sequences from an unsegmented stream, at real time speed. We demonstrate our approach by showing results comparable to state-of-the-art for action classification on the standard KTH-action set, and also on more challenging cluttered videos.