IEEE Transactions on Pattern Analysis and Machine Intelligence
A Bayesian Computer Vision System for Modeling Human Interactions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognition of Visual Activities and Interactions by Stochastic Parsing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognition of two-person interactions using a hierarchical Bayesian network
IWVS '03 First ACM SIGMM international workshop on Video surveillance
Human Motion: Modeling and Recognition of Actions and Interactions
3DPVT '04 Proceedings of the 3D Data Processing, Visualization, and Transmission, 2nd International Symposium
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
A Spectral Technique for Correspondence Problems Using Pairwise Constraints
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Recognition of Composite Human Activities through Context-Free Grammar Based Representation
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Towards optimal bag-of-features for object categorization and semantic video retrieval
Proceedings of the 6th ACM international conference on Image and video retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Information Retrieval for Music and Motion
Information Retrieval for Music and Motion
Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words
International Journal of Computer Vision
Human Pose Tracking in Monocular Sequence Using Multilevel Structured Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Event Modeling and Recognition Using Markov Logic Networks
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Spatial-Temporal correlatons for unsupervised action classification
WMVC '08 Proceedings of the 2008 IEEE Workshop on Motion and video Computing
Modeling and recognition of complex multi-person interactions in video
Proceedings of the 1st ACM international workshop on Multimodal pervasive video analysis
A stochastic graph evolution framework for robust multi-target tracking
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Query-based retrieval of complex activities using "strings of motion-words"
WMVC'09 Proceedings of the 2009 international conference on Motion and video computing
A large-scale benchmark dataset for event recognition in surveillance video
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Machine Recognition of Human Activities: A Survey
IEEE Transactions on Circuits and Systems for Video Technology
A "string of feature graphs" model for recognition of complex activities in natural videos
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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In this paper, a novel generalized framework of activity representation and recognition based on a 'string of feature graphs (SFG)' model is introduced. The proposed framework represents a visual activity as a string of feature graphs, where the string elements are initially matched using a graph-based spectral technique, followed by a dynamic programming scheme for matching the complete strings. The framework is motivated by success of time sequence analysis approaches in speech recognition, but modified in order to capture the spatio-temporal properties of individual actions, the interactions between objects, and speed of activity execution. This framework can be adapted to various spatio-temporal motion features, and we show details on using STIP features and track features. Furthermore, we show how this SFG model can be embedded within a switched dynamical system (SDS) that is able to automatically choose the most efficient features for a particular video segment. This allows us to analyze a variety of activities in natural videos in a computationally efficient manner. Experimental results on the basic SFG model as well as its integration with the SDS are shown on some of the most challenging multi-object datasets available to the activity analysis community.