PutMode: prediction of uncertain trajectories in moving objects
Date
2010
Authors
Qiao, Shaojie
Tang, Changjie
Jin, Huidong (Warren)
Long, Teng
Dai, Shucheng
Ku, Yungchang
Chau, Michael
Journal Title
Journal ISSN
Volume Title
Publisher
Springer
Abstract
Objective: Prediction of moving objects with uncertain motion patterns is emerging rapidly as a new exciting paradigm and is important for law enforcement applications such as criminal tracking analysis. However, existing algorithms for prediction in spatio-temporal databases focus on discovering frequent trajectory patterns from historical data. Moreover, these methods overlook the effect of some important factors, such as speed and moving direction. This lacks generality as moving objects may follow dynamic motion patterns in real life. Methods: We propose a framework for predicating uncertain trajectories in moving objects databases. Based on Continuous Time Bayesian Networks (CTBNs), we develop a trajectory prediction algorithm, called PutMode (Prediction of uncertain trajectories in Moving objects databases). It comprises three phases: (i) construction of TCTBNs (Trajectory CTBNs) which obey the Markov property and consist of states combined by three important variables including street identifier, speed, and direction; (ii) trajectory clustering for clearing up outlying trajectories; (iii) predicting the motion behaviors of moving objects in order to obtain the possible trajectories based on TCTBNs. Results: Experimental results show that PutMode can predict the possible motion curves of objects in an accurate and efficient manner in distinct trajectory data sets with an average accuracy higher than 80%. Furthermore, we illustrate the crucial role of trajectory clustering, which provides benefits on prediction time as well as prediction accuracy.
Description
Keywords
Keywords: Continuous time; CTBN; Dynamic motions; Historical data; Markov property; Motion behavior; Motion curve; Motion pattern; Moving direction; Moving objects; Moving objects databases; Prediction accuracy; Spatio-temporal database; Three phasis; Tracking anal CTBN; Moving objects databases; Trajectory clustering; Trajectory prediction
Citation
Collections
Source
Applied Intelligence
Type
Journal article
Book Title
Entity type
Access Statement
License Rights
Restricted until
2037-12-31
Downloads
File
Description