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Surveillance Video Indexing and Retrieval using Object Features and semantic Events

Abstract : In this paper, we propose an approach for surveillance video indexing and retrieval. The objective of this approach is to answer five main challenges we have met in this domain: (1) the lack of means for finding data from the indexed databases, (2) the lack of approaches working at different abstraction levels, (3) imprecise indexing, (4) incomplete indexing, (5) the lack of user-centered search. We propose a new data model containing two main types of extracted video contents: physical objects and events. Based on this data model we present a new rich and flexible query language. This language works at different abstraction levels, provides both exact and approximate matching and takes into account users' interest. In order to work with the imprecise indexing, two new methods respectively for object representation and object matching are proposed. Videos from two projects which have been partially indexed are used to validate the proposed approach. We have analyzed both query language usage and retrieval results. The obtained retrieval results are analyzed by the average normalized ranks are promising. The retrieval results at the object level are compared with another state of the art approach.
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Contributor : Monique Thonnat Connect in order to contact the contributor
Submitted on : Tuesday, July 20, 2010 - 9:47:11 AM
Last modification on : Friday, May 13, 2022 - 1:54:02 PM
Long-term archiving on: : Friday, October 22, 2010 - 12:12:42 PM


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  • HAL Id : inria-00502821, version 1



Thi Lan Le, Monique Thonnat, Alain Boucher, François Bremond. Surveillance Video Indexing and Retrieval using Object Features and semantic Events. International Journal of Pattern Recognition and Artificial Intelligence, 2009, 23 (7), pp 1439-1476. ⟨inria-00502821⟩



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