This paper proposes a video retrieval system from compressed outdoor video surveillance databases. The aim is to extract moving objects from frames provided by MPEG video stream in order to classify them into predefined categories according to image-based properties, and then robustly index them. The principal idea is to combine between useful properties of metrical classification and the notion of temporal consistency. Fuzzy geometry classification is used in order to provide an efficient method to classify motion regions into three generic categories: pedestrian, vehicle and no identified object. The temporal consistency provides a robust classification to changes of objects appearance and occlusion of object motion. The classified motion regions are used as templates for metrical training algorithms and as keys for tree indexing technique. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Khelifi, S. F., Elarbi Boudihir, M., & Nourine, R. (2005). Compressed telesurveillance video database retrieval using fuzzy classification system. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3656 LNCS, pp. 575–584). https://doi.org/10.1007/11559573_71
Mendeley helps you to discover research relevant for your work.