An efficient probabilistic context-free parsing algorithm that computes prefix probabilities
Computational Linguistics
A Flexible New Technique for Camera Calibration
IEEE Transactions on Pattern Analysis and Machine Intelligence
An efficient context-free parsing algorithm
Communications of the ACM
The theory of parsing, translation, and compiling
The theory of parsing, translation, and compiling
Action Recognition Using Probabilistic Parsing
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Bayesian Framework for Video Surveillance Application
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
A convenient multicamera self-calibration for virtual environments
Presence: Teleoperators and Virtual Environments
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
Speech and Language Processing (2nd Edition)
Speech and Language Processing (2nd Edition)
Activity Recognition using Dynamic Bayesian Networks with Automatic State Selection
WMVC '07 Proceedings of the IEEE Workshop on Motion and Video Computing
ICVS'03 Proceedings of the 3rd international conference on Computer vision systems
IEEE Transactions on Signal Processing
Image-enhanced multiple model tracking
Automatica (Journal of IFAC)
Trajectory-Based Anomalous Event Detection
IEEE Transactions on Circuits and Systems for Video Technology
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This paper considers the intent inference problem in a video surveillance application involving person tracking. The video surveillance is carried out through a distributed network of cameras with large non-overlapping areas. The target dynamics are formulated using a novel grammar-modulated state space model. We identify and model trajectory shapes of interest using the modeling framework of stochastic context-free grammars. The inference of such grammar models is performed using a Bayesian estimation algorithm called the Earley-Stolcke parser. The intent inference procedure is proposed at a meta-level, which allows the use of conventional trackers at the base level. The use of a suitable fusion scheme shows that intent inference using stochastic context-free grammars in a distributed camera network performs suitably even in the presence of large observation gaps and noisy observations.