A State-Based Approach to the Representation and Recognition of Gesture
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Hierarchical Hidden Markov Model: Analysis and Applications
Machine Learning
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
Recognizing multitasked activities from video using stochastic context-free grammar
Eighteenth national conference on Artificial intelligence
Representation and recognition of action in interactive spaces
Representation and recognition of action in interactive spaces
Semantic-level Understanding of Human Actions and Interactions using Event Hierarchy
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 1 - Volume 01
An Ontology for Video Event Representation
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 7 - Volume 07
Video-based event recognition: activity representation and probabilistic recognition methods
Computer Vision and Image Understanding - Special issue on event detection in video
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Simultaneous tracking of multiple body parts of interacting persons
Computer Vision and Image Understanding
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
Attribute Grammar-Based Event Recognition and Anomaly Detection
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Semantic Understanding of Continued and Recursive Human Activities
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Coupled Hidden Semi Markov Models for Activity Recognition
WMVC '07 Proceedings of the IEEE Workshop on Motion and Video Computing
Grounding the lexical semantics of verbs in visual perception using force dynamics and event logic
Journal of Artificial Intelligence Research
Robust human-computer interaction system guiding a user by providing feedback
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Automatic video interpretation: a novel algorithm for temporal scenario recognition
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Propagation networks for recognition of partially ordered sequential action
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Human action recognition using boosted EigenActions
Image and Vision Computing
A survey on vision-based human action recognition
Image and Vision Computing
A task-driven intelligent workspace system to provide guidance feedback
Computer Vision and Image Understanding
Towards high-level human activity recognition through computer vision and temporal logic
KI'10 Proceedings of the 33rd annual German conference on Advances in artificial intelligence
View independent recognition of human-vehicle interactions using 3-D models
WMVC'09 Proceedings of the 2009 international conference on Motion and video computing
Recognizing human action from a far field of view
WMVC'09 Proceedings of the 2009 international conference on Motion and video computing
Human activity analysis: A review
ACM Computing Surveys (CSUR)
On supervised human activity analysis for structured environments
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part III
Stochastic Representation and Recognition of High-Level Group Activities
International Journal of Computer Vision
Mining Layered Grammar Rules for Action Recognition
International Journal of Computer Vision
Probabilistic recognition of complex event
ICVS'11 Proceedings of the 8th international conference on Computer vision systems
A semantic-based probabilistic approach for real-time video event recognition
Computer Vision and Image Understanding
Describing video contents in natural language
HYBRID '12 Proceedings of the Workshop on Innovative Hybrid Approaches to the Processing of Textual Data
An information retrieval approach to identifying infrequent events in surveillance video
Proceedings of the 3rd ACM conference on International conference on multimedia retrieval
ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume 2
Video event description in scene context
Neurocomputing
Personal driving diary: Automated recognition of driving events from first-person videos
Computer Vision and Image Understanding
Skin detection by dual maximization of detectors agreement for video monitoring
Pattern Recognition Letters
Max-Margin Early Event Detectors
International Journal of Computer Vision
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This paper describes a methodology for automated recognition of complex human activities. The paper proposes a general framework which reliably recognizes high-level human actions and human-human interactions. Our approach is a description-based approach, which enables a user to encode the structure of a high-level human activity as a formal representation. Recognition of human activities is done by semantically matching constructed representations with actual observations. The methodology uses a context-free grammar (CFG) based representation scheme as a formal syntax for representing composite activities. Our CFG-based representation enables us to define complex human activities based on simpler activities or movements. Our system takes advantage of both statistical recognition techniques from computer vision and knowledge representation concepts from traditional artificial intelligence. In the low-level of the system, image sequences are processed to extract poses and gestures. Based on the recognition of gestures, the high-level of the system hierarchically recognizes composite actions and interactions occurring in a sequence of image frames. The concept of hallucinations and a probabilistic semantic-level recognition algorithm is introduced to cope with imperfect lower-layers. As a result, the system recognizes human activities including `fighting' and `assault', which are high-level activities that previous systems had difficulties. The experimental results show that our system reliably recognizes sequences of complex human activities with a high recognition rate.