Title :
An efficient method for detecting ghost and left objects in surveillance video
Author :
Lu, Sijun ; Zhang, Jian ; Feng, David
Author_Institution :
Nat. ICT Australia (NICTA), Sydney
Abstract :
This paper proposes an efficient method for detecting ghost and left objects in surveillance video, which, if not identified, may lead to errors or wasted computation in background modeling and object tracking in surveillance systems. This method contains two main steps: the first one is to detect stationary objects, which narrows down the evaluation targets to a very small number of foreground blobs; the second step is to discriminate the candidates between ghost and left objects. For the first step, we introduce a novel stationary object detection method based on continuous object tracking and shape matching. For the second step, we propose a fast and robust inpainting method to differentiate between ghost and left objects by constructing the real background using the candidate ´s corresponding regions in the input and the background images. The effectiveness of our method has been validated by experiments over a variety of video sequences.
Keywords :
image matching; image sequences; object detection; video surveillance; background modeling; ghost detection; object detection; object tracking; robust inpainting method; shape matching; video sequences; video surveillance; Object detection; Surveillance;
Conference_Titel :
Advanced Video and Signal Based Surveillance, 2007. AVSS 2007. IEEE Conference on
Conference_Location :
London
Print_ISBN :
978-1-4244-1696-7
Electronic_ISBN :
978-1-4244-1696-7
DOI :
10.1109/AVSS.2007.4425368