|
|
| Identifier |
000369011 |
| Title |
Using Constraint Optimization for Conflict Resolution and Detail Control in Activity Recognition |
| Alternative Title |
Χρήση Βελτιστοποίησης Περιορισμών για Επίλυση Συγκρούσεων και Έλεγχο Λεπτομέρειας στην Αναγνώριση Δραστηριοτήτων |
|
Author
|
Φιλιππάκη, Χρυσή Χαράλαμπος
|
|
Thesis advisor
|
Αντωνίου, Γρηγόρης
Τσαμαρδινός, Ιωάννης
|
| Abstract |
Activity recognition is an open domain which can be used in a wide spectrum of applications,
ranging from video surveillance to monitoring and assisting children, elderly
or sick people. Some prior work on activity recognition focuses on identifying all
occurrences of a specific type of activity. In more general activity recognition settings
however (e.g., Ambient Assisted Living), a system should identify all of user activities
and their relations, e.g. their temporal sequence or subsumption (for activities hierarchically
organized). Due to noise or other imperfections a naïve recognition system
may report activities that are logically inconsistent with each other, such as the
user is sleeping on the couch and at the same time is watching TV.
In this work, we develop a rule-based activity recognition system for hierarchically-
organized complex activities that returns only logically consistent sets of activities
(scenarios). This is achieved by explicitly formulating activity conflicts as a
weighted partial maximum satisfiability problem (Weighted Partial MaxSAT) such
that any solution to it corresponds to a set of identified activities (scenario) that do not
conflict. The system also has the ability to adjust the desired level of detail of the scenarios
returned, e.g., (1 hour TV-watching, 1’ phone-call, 2 hours TV-watching) vs. (3
hours of TV-watching). This is accomplished by assigning preferences to clauses of
the Weighted Partial MaxSAT problem. Each complex activity’s weight is calculated
by taking into account its confidence, temporal duration and number of used atomic
activities. We note that the optimization techniques presented could accommodate
other types of preferences and be generalized to other settings. The system is implemented
and evaluated in an Ambient Intelligence experimental space.
|
| Language |
English |
| Subject |
Activity Recognition |
|
Ambient Intelligence |
|
Αναγνώριση δραστηριοτήτων |
|
Περιβάλλοντα διάχυτης νοημοσύνης |
| Issue date |
2011 |
| Collection
|
School/Department--School of Sciences and Engineering--Department of Computer Science--Post-graduate theses
|
|
|
Type of Work--Post-graduate theses
|
| Views |
629 |
Using Constraint Optimization for Conflict Resolution and Detail Control in Activity Recognition
