Authors:
Antonio d'Acierno
1
;
Marco Leone
2
;
Alessia Saggese
2
and
Mario Vento
2
Affiliations:
1
National Research Council, Italy
;
2
University of Salerno, Italy
Keyword(s):
Spatio-temporal Queries, Spatio-temporal Data Indexing, Information Retrieval.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Pre-Processing and Post-Processing for Data Mining
;
Software Development
;
Symbolic Systems
Abstract:
Moving people’s and objects’ trajectories extracted from video sequences are increasingly assuming a key role for detecting anomalous events and for characterizing human behaviors. Among the key related issues, there is the need of efficiently storing a huge amount of 3D trajectories together with retrieval techniques sufficiently fast to allow a real-time extraction of trajectories satisfying spatio-temporal requirements. Unfortunately, while exist well established solutions for 2D trajectories, theoretical solutions proposed for 3D ones are not widely available in commercial and free spatially enabled DBMS; the paper thus presents a novel method for extending available 2D indexes to 3D data. In particular, starting from a redundant bi-dimensional indexing scheme recently introduced in (d’Acierno et al., 2011), we propose a new retrieval system that, while still using off-the-shelf solutions, avoids almost any redundancy in data to be handled; both the spatial complexity and the ret
rieval efficiency for time-interval queries have been significantly improved.
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