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Improved Subject Identification in Surveillance Video Using Super-Resolution
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Author(s): Simon Denman (Queensland University of Technology, Australia), Frank Lin (Queensland University of Technology, Australia), Vinod Chandran (Queensland University of Technology, Australia), Sridha Sridharan (Queensland University of Technology, Australia)and Clinton Fookes (Queensland University of Technology, Australia)
Copyright: 2013
Pages: 44
Source title:
Multimedia Networking and Coding
Source Author(s)/Editor(s): Reuben A. Farrugia (University of Malta, Malta)and Carl J. Debono (University of Malta, Malta)
DOI: 10.4018/978-1-4666-2660-7.ch011
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Abstract
The time consuming and labour intensive task of identifying individuals in surveillance video is often challenged by poor resolution and the sheer volume of stored video. Faces or identifying marks such as tattoos are often too coarse for direct matching by machine or human vision. Object tracking and super-resolution can then be combined to facilitate the automated detection and enhancement of areas of interest. The object tracking process enables the automatic detection of people of interest, greatly reducing the amount of data for super-resolution. Smaller regions such as faces can also be tracked. A number of instances of such regions can then be utilized to obtain a super-resolved version for matching. Performance improvement from super-resolution is demonstrated using a face verification task. It is shown that there is a consistent improvement of approximately 7% in verification accuracy, using both Eigenface and Elastic Bunch Graph Matching approaches for automatic face verification, starting from faces with an eye to eye distance of 14 pixels. Visual improvement in image fidelity from super-resolved images over low-resolution and interpolated images is demonstrated on a small database. Current research and future directions in this area are also summarized.
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