Foreground object detection from videos containing complex background
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
ICAPR'05 Proceedings of the Third international conference on Pattern Recognition and Image Analysis - Volume Part II
A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications
IEEE Transactions on Image Processing
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In this paper we present a model-based algorithm working as a post-processing phase of any foreground object detector. The model is suited to recover camouflage errors producing the segmentation of an entity in small and unconnected parts. The model does not require training procedures, but only information about the estimated size of the person, obtainable when an inverse perspective mapping procedure is used. A quantitative evaluation of the effectiveness of the method, used after four well known moving object detection algorithms has been carried out. Performance are given on a variety of publicly available databases, selected among those presenting highly camouflaged objects in real scenes referring to both indoor and outdoor environments.