From image sequences towards conceptual descriptions
Image and Vision Computing
The Representation Space Paradigm of Concurrent Evolving Object Descriptions
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Automatic symbolic traffic scene analysis using belief networks
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graphical models for recognizing human interactions
Proceedings of the 1998 conference on Advances in neural information processing systems II
A Guide to the Literature on Learning Probabilistic Networks from Data
IEEE Transactions on Knowledge and Data Engineering
Coupled hidden Markov models for complex action recognition
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
LAFTER: Lips and Face Real-Time Tracker
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Probabilistic visual learning for object detection
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Active Gesture Recognition Using Partially Observable markov Decision Processes
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume III-Volume 7276 - Volume 7276
Probabilistic Independence Networks for Hidden Markov Probability Models
Probabilistic Independence Networks for Hidden Markov Probability Models
Building Qualitative Event Models Automatically from Visual Input
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Operations for learning with graphical models
Journal of Artificial Intelligence Research
Face Image Retrieval Using HMMs
CBAIVL '99 Proceedings of the IEEE Workshop on Content-Based Access of Image and Video Libraries
Unusual Activity Analysis in Video Sequences
RSFDGrC '07 Proceedings of the 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Video Background Segmentation Using Adaptive Background Models
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
Modeling and recognition of complex multi-person interactions in video
Proceedings of the 1st ACM international workshop on Multimodal pervasive video analysis
Physics-based activity modelling in phase space
Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing
Modeling multi-object activities in phase space
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume Part I
Background modeling via incremental maximum margin criterion
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume part II
Smooth foreground-background segmentation for video processing
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
Recognizing events in an automated surveillance system
MRCS'06 Proceedings of the 2006 international conference on Multimedia Content Representation, Classification and Security
Foreground detection by robust PCA solved via a linearized alternating direction method
ICIAR'12 Proceedings of the 9th international conference on Image Analysis and Recognition - Volume Part I
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We describe a real-time computer vision and machine learning system for modeling and recognizing human behaviors in a visual surveillance task. The system is particularly concerned with detecting when interactions between people occur, and classifying the type of interaction. Examples of interesting interaction behaviors include following another person, altering one's path to meet another, and so forth. Our system combines top-down with bottom-up information in a closed feedback loop, with both components employing a statistical Bayesian approach. We propose and compare two different state-based learning architectures, namely HMMs and CHMMs, for modeling behaviors and interactions. The CHMM model is shown to work much more efficiently and accurately. Finally, to deal with the problem of limited training data, a synthetic 'Alife-style' training system is used to develop flexible prior models for recognizing human interactions. We demonstrate the ability to use these a priori models to accurately classify real human behaviors and interactions with no additional tuning or training.