Open AccessProceedings Article
Automatic Tracking, Super-Resolution and Recognition of Human Faces from Surveillance Video
Frank Lin,Simon Denman,Vinod Chandran,Subramanian Sridharan +3 more
- pp 37-40
TLDR
In this article, 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.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.read more
Citations
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Journal ArticleDOI
Super-resolution: a comprehensive survey
TL;DR: The current comprehensive survey provides an overview of most of these published works by grouping them in a broad taxonomy, and common issues in super-resolution algorithms, such as imaging models and registration algorithms, optimization of the cost functions employed, dealing with color information, improvement factors, assessment of super- resolution algorithms, and the most commonly employed databases are discussed.
Facial Descriptors for Identity-Preserving Multiple People Tracking
TL;DR: Facial descriptors can be used very effectively in conjunction with a tracklet-based multi-person tracker both to localize and to identify or re-identify people over long sequences and can reliably deliver both trajectories and identities in crowded scenes.
Proceedings ArticleDOI
Comparison of reconstruction and example-based Super-Resolution
TL;DR: From the experimental results on test images, EBSR preserves the structure of the original image compared to RBSR, and is preferred except when there are large motions in consecutive frames.
Book ChapterDOI
Improved subject identification in surveillance video using super resolution
TL;DR: 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.
Book Chapter
Improved Subject Identification in Surveillance Video using Super Resolution
TL;DR: In this article, 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.
References
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Journal ArticleDOI
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