Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
Parameterized modeling and recognition of activities
Computer Vision and Image Understanding
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
The Recognition of Human Movement Using Temporal Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stochastic Complexity in Statistical Inquiry Theory
Stochastic Complexity in Statistical Inquiry Theory
A Mixed-State Condensation Tracker with Automatic Model-Switching
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Learning and inferring transportation routines
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Robust shape tracking in the presence of cluttered background
IEEE Transactions on Multimedia
A survey on visual surveillance of object motion and behaviors
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Human expression recognition from motion using a radial basis function network architecture
IEEE Transactions on Neural Networks
Trajectory Modeling Using Mixtures of Vector Fields
IbPRIA '09 Proceedings of the 4th Iberian Conference on Pattern Recognition and Image Analysis
Trajectory classification using switched dynamical hidden Markov models
IEEE Transactions on Image Processing
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This paper describes an algorithm for classifying human motion patterns (trajectories) observed in video sequences. We address this task in a hierarchical way: high-level activities are described as sequences of low-level motion patterns (dynamic models). These low-level dynamic models are simply independent increment processes, each describing a specific motion regime (e.g., ''moving left''). Classifying a trajectory thus consists in segmenting it into the sequence its low-level components; each sequence of low-level components corresponds to a high-level activity. To perform the segmentation, we introduce a penalized maximum-likelihood criterion which is able to select the number of segments via a novel MDL-type penalty. Experiments with synthetic and real data illustrate the effectiveness of the proposed approach.