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Automatic tracking, super-resolution and recognition of human faces from surveillance video

Lin, Frank C., Denman, Simon, Chandran, Vinod, & Sridharan, Sridha (2007) Automatic tracking, super-resolution and recognition of human faces from surveillance video. In Proceedings of IAPR Conference on Machine Vision Applications 2007, The International Association for Pattern Recognition, Institute of Industrial Science, The University of Tokyo, pp. 37-40.

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Abstract

Identifying an individual from surveillance video is a difficult, time consuming and labour intensive process. The proposed system aims to streamline this process by filtering out unwanted scenes and enhancing an individual's face through super-resolution. An automatic face recognition system is then used to identify the subject or present the human operator with likely matches from a database. A person tracker is used to speed up the subject detection and super-resolution process by tracking moving subjects and cropping a region of interest around the subject's face to reduce the number and size of the image frames to be super-resolved respectively. In this paper, experiments have been conducted to demonstrate how the optical flow super-resolution method used improves surveillance imagery for visual inspection as well as automatic face recognition on an Eigenface and Elastic Bunch Graph Matching system. The optical flow based method has also been benchmarked against the ``hallucination'' algorithm, interpolation methods and the original low-resolution images. Results show that both super-resolution algorithms improved recognition rates significantly. Although the hallucination method resulted in slightly higher recognition rates, the optical flow method produced less artifacts and more visually correct images suitable for human consumption.

Impact and interest:

2 citations in Scopus
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ID Code: 31709
Item Type: Conference Paper
Keywords: Super Resolution, Surveillance, Optical Flow, Object Tracking, Identification
DOI: 10.1.1.143.9017
ISBN: 9784901122078
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 2007 [please consult the authors]
Deposited On: 13 Apr 2010 07:47
Last Modified: 29 Feb 2012 23:32

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