Blocking Gibbs sampling in very large probabilistic expert systems
International Journal of Human-Computer Studies - Special issue: real-world applications of uncertain reasoning
Scaling up dynamic time warping for datamining applications
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Generation of Semantic Regions from Image Sequences
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume II - Volume II
Automatic Learning of an Activity-Based Semantic Scene Model
AVSS '03 Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance
The Journal of Machine Learning Research
Multi-person localization and track assignment in overlapping camera views
DAGM'11 Proceedings of the 33rd international conference on Pattern recognition
Learning semantic scene models by trajectory analysis
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Multi-camera open space human activity discovery for anomaly detection
AVSS '11 Proceedings of the 2011 8th IEEE International Conference on Advanced Video and Signal Based Surveillance
Bayesian Nonparametric Inference of Switching Dynamic Linear Models
IEEE Transactions on Signal Processing
Cross-domain traffic scene understanding by motion model transfer
Proceedings of the 4th ACM/IEEE international workshop on Analysis and retrieval of tracked events and motion in imagery stream
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We present a novel non-parametric Bayesian model to jointly discover the dynamics of low-level actions and high-level behaviors of tracked people in open environments. Our model represents behaviors as Markov chains of actions which capture high-level temporal dynamics. Actions may be shared by various behaviors and represent spatially localized occurrences of a person's low-level motion dynamics using Switching Linear Dynamics Systems. Since the model handles real-valued features directly, we do not lose information by quantizing measurements to 'visual words' and can thus discover variations in standing, walking and running without discrete thresholds. We describe inference using Gibbs sampling and validate our approach on several artificial and real-world tracking datasets. We show that our model can distinguish relevant behavior patterns that an existing state-of-the-art method for hierarchical clustering cannot.