Learning variable-length Markov models of behavior
Computer Vision and Image Understanding - Modeling people toward vision-based underatanding of a person's shape, appearance, and movement
Complexity classifications of boolean constraint satisfaction problems
Complexity classifications of boolean constraint satisfaction problems
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
Discriminative training and maximum entropy models for statistical machine translation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Learning structured prediction models: a large margin approach
Learning structured prediction models: a large margin approach
Integer linear programming inference for conditional random fields
ICML '05 Proceedings of the 22nd international conference on Machine learning
Fine-Grained Activity Recognition by Aggregating Abstract Object Usage
ISWC '05 Proceedings of the Ninth IEEE International Symposium on Wearable Computers
Machine Learning
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Ontology-Based User Intention Recognition for Proactive Planning of Intelligent Robot Behavior
MUE '08 Proceedings of the 2008 International Conference on Multimedia and Ubiquitous Engineering
Real world activity recognition with multiple goals
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Improving the recognition of interleaved activities
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Event Modeling and Recognition Using Markov Logic Networks
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Activity Recognition from Sparsely Labeled Data Using Multi-Instance Learning
LoCA '09 Proceedings of the 4th International Symposium on Location and Context Awareness
Context-Aware Activity Recognition through a Combination of Ontological and Statistical Reasoning
UIC '09 Proceedings of the 6th International Conference on Ubiquitous Intelligence and Computing
Collective semantic role labelling with Markov logic
CoNLL '08 Proceedings of the Twelfth Conference on Computational Natural Language Learning
CIGAR: concurrent and interleaving goal and activity recognition
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Recognizing daily activities with RFID-based sensors
Proceedings of the 11th international conference on Ubiquitous computing
Human Activity Recognition and Pattern Discovery
IEEE Pervasive Computing
An unsupervised approach to activity recognition and segmentation based on object-use fingerprints
Data & Knowledge Engineering
Recognizing activities with multiple cues
Proceedings of the 2nd conference on Human motion: understanding, modeling, capture and animation
Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning
Recognizing interleaved and concurrent activities: A statistical-relational approach
PERCOM '11 Proceedings of the 2011 IEEE International Conference on Pervasive Computing and Communications
A knowledge-driven approach to composite activity recognition in smart environments
UCAmI'12 Proceedings of the 6th international conference on Ubiquitous Computing and Ambient Intelligence
Complex activity recognition using context-driven activity theory and activity signatures
ACM Transactions on Computer-Human Interaction (TOCHI)
A probabilistic ontological framework for the recognition of multilevel human activities
Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing
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The majority of approaches to activity recognition in sensor environments are either based on manually constructed rules for recognizing activities or lack the ability to incorporate complex temporal dependencies. Furthermore, in many cases, the rather unrealistic assumption is made that the subject carries out only one activity at a time. In this paper, we describe the use of Markov logic as a declarative framework for recognizing interleaved and concurrent activities incorporating both input from pervasive lightweight sensor technology and common-sense background knowledge. In particular, we assess its ability to learn statistical-temporal models from training data and to combine these models with background knowledge to improve the overall recognition accuracy. We also show the viability and the benefit of exploiting both qualitative and quantitative temporal relationships like the duration of the activities and their temporal order. To this end, we propose two Markov logic formulations for inferring the foreground activity as well as each activities' start and end times. We evaluate the approach on an established dataset where it outperforms state-of-the-art algorithms for activity recognition.