An analysis of first-order logics of probability
Artificial Intelligence
A tutorial on learning with Bayesian networks
Learning in graphical models
A Bayesian Computer Vision System for Modeling Human Interactions
IEEE Transactions on Pattern Analysis and Machine Intelligence
A framework for recognizing the simultaneous aspects of American sign language
Computer Vision and Image Understanding - Modeling people toward vision-based underatanding of a person's shape, appearance, and movement
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
Semi-Supervised Adapted HMMs for Unusual Event Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Activity Recognition and Abnormality Detection with the Switching Hidden Semi-Markov Model
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Machine Learning
Beyond Tracking: Modelling Activity and Understanding Behaviour
International Journal of Computer Vision
Video Behavior Profiling for Anomaly Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Event Modeling and Recognition Using Markov Logic Networks
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Recognizing activities with multiple cues
Proceedings of the 2nd conference on Human motion: understanding, modeling, capture and animation
The Bayesian structural EM algorithm
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Human activity recognition with trajectory data in multi-floor indoor environment
RSKT'12 Proceedings of the 7th international conference on Rough Sets and Knowledge Technology
Visual code-sentences: a new video representation based on image descriptor sequences
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part I
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In this paper, we propose solutions on learning dynamic Bayesian network (DBN) with domain knowledge for human activity recognition. Different types of domain knowledge, in terms of first order probabilistic logics (FOPLs), are exploited to guide the DBN learning process. The FOPLs are transformed into two types of model priors: structure prior and parameter constraints. We present a structure learning algorithm, constrained structural EM (CSEM), on learning the model structures combining the training data with these priors. Our method successfully alleviates the common problem of lack of sufficient training data in activity recognition. The experimental results demonstrate simple logic knowledge can compensate effectively for the shortage of the training data and therefore reduce our dependencies on training data.