Using constraint optimization for conflict resolution and detail control in activity recognition

Authors:
Chrysi Filippaki;Grigoris Antoniou;Ioannis Tsamardinos
Affiliations:
Computer Science Department, University of Crete, Greece;Computer Science Department, University of Crete, Greece;Computer Science Department, University of Crete, Greece
Venue:
AmI'11 Proceedings of the Second international conference on Ambient Intelligence
Year:
2011

Citing 9
Cited 1

Learning and Detecting Activities from Movement Trajectories Using the Hierarchical Hidden Markov Models

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Markov logic networks

Machine Learning
Jess in action: rule-based systems in java

Jess in action: rule-based systems in java
Event Modeling and Recognition Using Markov Logic Networks

ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
A Localized Approach to Abandoned Luggage Detection with Foreground-Mask Sampling

AVSS '08 Proceedings of the 2008 IEEE Fifth International Conference on Advanced Video and Signal Based Surveillance
Chronicle recognition improvement using temporal focusing and hierarchization

IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Recognizing activities with multiple cues

Proceedings of the 2nd conference on Human motion: understanding, modeling, capture and animation
A logic programming approach to activity recognition

Proceedings of the 2nd ACM international workshop on Events in multimedia
Recognizing interleaved and concurrent activities: A statistical-relational approach

PERCOM '11 Proceedings of the 2011 IEEE International Conference on Pervasive Computing and Communications

An evidential fusion approach for activity recognition in ambient intelligence environments

Robotics and Autonomous Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

In Ambient Assisted Living and other environments the problem is to recognize all of user activities. Due to noisy or incomplete information a naïve recognition system may report activities that are logically inconsistent with each other, e.g., the user is sleeping on the couch and at the same time is watching TV. In this work, we develop a rule-based recognition system for hierarchically-organized activities that returns only logically consistent scenarios. This is achieved by explicitly formulating conflicts as Weighted Partial MaxSAT clauses to be satisfied. The system also has the ability to adjust the desired level of detail of the scenarios returned. This is accomplished by assigning preferences to clauses of the SAT problem. The system is implemented and evaluated in a real Ambient Intelligence experimental space. It is shown to be robust to the presence of noise; the level of detail can easily be adjusted by the use of two preference parameters.