QUT ePrints

Investigation into optical flow super-resolution for surveillance applications

Lin, Frank C., Fookes, Clinton B., Chandran, Vinod, & Sridharan, Sridha (2005) Investigation into optical flow super-resolution for surveillance applications. In Lovell, Brian C. & Maeder, Anthony J. (Eds.) APRS Workshop on Digital Image Computing: Pattern Recognition and Imaging for Medical Applications, 21 February, 2005, Brisbane.

View at publisher

Abstract

Video surveillance systems are becoming an indispensable tool in today's environment, particularly for security related applications. Surveillance footage is often routinely used to identify faces of criminals "caught in the act" or for tracking individuals in a crowded environment. Most face images captured with these systems however, are small and coarse, making it extremely difficult to identify an individual through human observation or via automatic face recognition systems. Super-resolution (SR) is a technique that can overcome this limitation by combining complimentary information from several frames of a video sequence to produce high resolution images of a subject. A problem that plagues many existing SR systems is that they can only deal with simple, rigid inter-frame transformations, thus performing poorly with face images as faces are non-planar, non-rigid, non-lambertian and can self-occlude. This paper presents a SR system to overcome these limitations by using a robust optical flow technique. An investigation into the quality of the super-resolved images and their dependency on the number of video sequence frames used in the reconstruction is undertaken. Different fusion techniques are also investigated and experiments are conducted over two image sequences. Results show significant improvement of the image quality and resolution over the original low resolution sequences.

Impact and interest:

Citation countsare sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

Full-text downloads:

285 since deposited on 17 Feb 2009
38 in the past twelve months

Full-text downloadsdisplays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.

ID Code: 17945
Item Type: Conference Paper
Additional Information: The contents of this conference can be freely accessed online via the conference's web page (see hypertext link).
Keywords: Super-Resolution, Optical Flow, Intelligent Surveillance
ISBN: 0958025533
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Computer Vision (080104)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Image Processing (080106)
Divisions: Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
Past > Institutes > Information Security Institute
Past > Schools > School of Engineering Systems
Copyright Owner: Copyright 2005 The Australian Pattern Recognition Society
Deposited On: 17 Feb 2009 12:50
Last Modified: 29 Feb 2012 23:11

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page