Comparison of Background Models for Video Surveillance
作者： Matthew Fettke , Fangpo He , Karl Sammut , Matthew Naylor
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摘要: Background modelling is a common form of motion detection employed by many autonomous video surveillance systems. Accurately the background challenging task, particularly for outdoor scenes where factors such as and camera shake can cause mistaken foreground objects. Recent research has developed models that are capable detecting in real-time while ignoring most motion, but it not clear how well these would perform on exhibit typical problems. The aim this paper to assess performance leading (namely , Hybrid Detection Algorithm, Three-frame Temporal Difference), using sequences contain problems which trouble existing strengths weaknessesof reported analysed, with identifying suitable directions development robust
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