A Modeling and Similarity Measure Function for Multiple Trajectories in Moving Databases

• Published in: Computational Science and Its Applications - ICCSA 2006 pp. 114 - 124
• Main Authors: Shim, Choon-Bo, Kim, John
• Format: Book Chapter Conference Proceeding
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2006; Springer
• Series: Lecture Notes in Computer Science
• Subjects: Algorithmics. Computability. Computer arithmetics; Applied sciences; Artificial intelligence; Computer science; control theory; systems; Exact sciences and technology; Information systems. Data bases; Memory organisation. Data processing; Pattern recognition. Digital image processing. Computational geometry; Software; Theoretical computing; Multimodel; Content based retrieval; Similarity; Image processing; Distributed database; Soccer; Information retrieval; Multidatabase retrieval; Topology; Modeling; Video databases; Sport; Semantics; Object oriented; Time interval; Metric; Trajectory; Moving body; Object location
• ISBN: 3540340726; 9783540340720; 354034070X; 9783540340706
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/11751588_13

In this paper, we focus on a new spatio-temporal representation scheme which can efficiently model multiple trajectories based on several moving objects in video databases. The traditional methods only consider direction property, time interval property, and spatial relations property for modeling moving objects’ trajectories. But, our method also takes into account on distance property, conceptual location information, and related object information (e.g. player name having a soccer ball). In addition, we propose a similarity measure function that improves a retrieval accuracy to measure a similarity among multiple trajectories. The proposed scheme supports content-based retrieval using moving objects’ trajectories and supports semantics-based retrieval using concepts which are acquired through the location information of moving objects. Finally, from the experimental results using real trajectories extracted from soccer video data with soccer ball and player, the performance of our scheme achieves about 15-20% performance improvement against existing schemes when the weights of angle and topological relation are over two times than that of distance.