Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A tutorial on hidden Markov models and selected applications in speech recognition
Readings in speech recognition
Contextual Priming for Object Detection
International Journal of Computer Vision
Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Learning the Statistics of People in Images and Video
International Journal of Computer Vision - Special Issue on Computational Vision at Brown University
Context-based vision system for place and object recognition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Fast Pose Estimation with Parameter-Sensitive Hashing
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Mixtures of deterministic-probabilistic networks and their AND/OR search space
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Case-factor diagrams for structured probabilistic modeling
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Inferring Activities from Interactions with Objects
IEEE Pervasive Computing
Shape Matching and Object Recognition Using Low Distortion Correspondences
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Strike a Pose: Tracking People by Finding Stylized Poses
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Vector Boosting for Rotation Invariant Multi-View Face Detection
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Detecting Irregularities in Images and in Video
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Recovering Human Body Configurations Using Pairwise Constraints between Parts
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Machine Learning
One-Shot Learning of Object Categories
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiclass Object Recognition with Sparse, Localized Features
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Hidden Conditional Random Fields for Gesture Recognition
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Using Multiple Segmentations to Discover Objects and their Extent in Image Collections
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Shape matching and object recognition
Shape matching and object recognition
Extracting Places and Activities from GPS Traces Using Hierarchical Conditional Random Fields
International Journal of Robotics Research
Towards efficient sampling: exploiting random walk strategies
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
The role of context in head gesture recognition
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Approximate inference for first-order probabilistic languages
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Representing cyclic human motion using functional analysis
Image and Vision Computing
Recovering human body configurations: combining segmentation and recognition
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Predicting 3d people from 2d pictures
AMDO'06 Proceedings of the 4th international conference on Articulated Motion and Deformable Objects
New advances in inference by recursive conditioning
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Value elimination: bayesian inference via backtracking search
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Learning models of relational stochastic processes
ECML'05 Proceedings of the 16th European conference on Machine Learning
A boundary-fragment-model for object detection
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Logic-based representation, reasoning and machine learning for event recognition
Proceedings of the Fourth ACM International Conference on Distributed Event-Based Systems
Knowledge based activity recognition with dynamic bayesian network
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Don't fear optimality: sampling for probabilistic-logic sequence models
ILP'09 Proceedings of the 19th international conference on Inductive logic programming
On complex event processing for real-time situational awareness
RuleML'2011 Proceedings of the 5th international conference on Rule-based reasoning, programming, and applications
Pervasive and Mobile Computing
Probabilistic event calculus based on Markov logic networks
RuleML'11 Proceedings of the 5th international conference on Rule-based modeling and computing on the semantic web
Using constraint optimization for conflict resolution and detail control in activity recognition
AmI'11 Proceedings of the Second international conference on Ambient Intelligence
Event processing under uncertainty
Proceedings of the 6th ACM International Conference on Distributed Event-Based Systems
Location-based reasoning about complex multi-agent behavior
Journal of Artificial Intelligence Research
Complex event processing over distributed probabilistic event streams
Computers & Mathematics with Applications
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In this paper, we introduce a first-order probabilistic model that combines multiple cues to classify human activities from video data accurately and robustly. Our system works in a realistic office setting with background clutter, natural illumination, different people, and partial occlusion. The model we present is compact, requires only fifteen sentences of first-order logic grouped as a Dynamic Markov Logic Network (DMLNs) to implement the probabilistic model and leverages existing state-of-the-art work in pose detection and object recognition.