Abstract
A novel method for content-based retrieval of surveillance video data is presented. The study starts from the realistic assumption that the automatic feature extraction is kept simple, i.e. only segmentation and low-cost filtering operations have been applied.
The solution is based on a new and generic dissimilarity measure for discriminating video surveillance scenes. This weighted compound measure can be interactively adapted during a session in order to capture the user’s subjectivity. Upon this, a key-frame selection and a content-based retrieval system have been developed and tested on several actual surveillance sequences. Experiments have shown how the proposed method is efficient and robust to segmentation errors.
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Meessen, J., Coulanges, M., Desurmont, X., Delaigle, JF. (2006). Content-Based Retrieval of Video Surveillance Scenes. In: Gunsel, B., Jain, A.K., Tekalp, A.M., Sankur, B. (eds) Multimedia Content Representation, Classification and Security. MRCS 2006. Lecture Notes in Computer Science, vol 4105. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11848035_103
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DOI: https://doi.org/10.1007/11848035_103
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-39392-4
Online ISBN: 978-3-540-39393-1
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