Indexation et recherche de vidéo pour la vidéosurveillance

Thi Lan Le 1
Abstract : The goal of this work is to propose a general approach for surveillance video indexing and retrieval. Based on the hypothesis that videos are preprocessed by an external video analysis module, this approach is composed of two phases : indexing phase and retrieval phase. In order to profit from the output of various video analysis modules, a general data model consisting of two main concepts, objects and events, is proposed. The indexing phase that aims at preparing data defined in the data model performs three tasks. Firstly, two new key blob detection methods in the object representation task choose for each detected object a set of key blobs associated with a weight. Secondly, the feature extraction task analyzes a number of visual and temporal features on detected objects. Finally, the indexing task computes attributes of the two concepts and stores them in the database. The retrieval phase starts with a user query and is composed of 4 tasks. In the formulation task, user expresses his query in a new rich query language. This query is then analyzed by the syntax parsing task. A new matching method in the matching task aims at retrieving effectively relevant results. Two proposed methods in the relevance feedback task allow to interact with the user in order to improve retrieved results. The key blob detection method has improved results of one method in the state of the art. The analysis of query language usage shows that many queries at different abstraction levels can be expressed. The matching method has proved its performance in comparison with two other methods in the state of the art. The complete approach has been validated on two video databases coming from two projects : CARETAKER and CAVIAR. Videos of the CARETAKER project are analyzed by the VSIP platform of the Pulsar team while videos coming from CAVIAR project are manually annotated. Experiments have shown how the proposed approach is efficient and robust to retrieve the objects of interest and the complex events from surveillance videos.
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Submitted on : Wednesday, June 10, 2009 - 5:46:44 AM
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Thi Lan Le. Indexation et recherche de vidéo pour la vidéosurveillance. Interface homme-machine [cs.HC]. Université Nice Sophia Antipolis, 2009. Français. ⟨tel-00393866⟩

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