Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/25840
Title: Detecting ghost and left objects in surveillance video
Authors: Lu, S
Zhang, J
Feng, DD
Keywords: Ghost
Left object
Inpainting
Background modeling
Motion detection
Object tracking
Issue Date: 2009
Publisher: World Scientific
Source: International journal of pattern recognition and artificial intelligence, 2009, v. 23, no. 7, p. 1503-1525 How to cite?
Journal: International journal of pattern recognition and artificial intelligence 
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 computational power in background modeling and object tracking in video 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 regions in the input image; 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 reconstructing the real background using the candidate's corresponding regions in the current input and background image. The effectiveness of our method has been validated by experiments over a variety of video sequences and comparisons with existing state-of-art methods.
URI: http://hdl.handle.net/10397/25840
ISSN: 0218-0014
EISSN: 1793-6381
DOI: 10.1142/S021800140900765X
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

3
Citations as of Sep 15, 2017

WEB OF SCIENCETM
Citations

1
Last Week
0
Last month
0
Citations as of Sep 17, 2017

Page view(s)

30
Last Week
0
Last month
Checked on Sep 17, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.