Journal article
Real time surveillance for low resolution and limited data scenarios: An image set classification approach
Information Sciences, Vol.580, pp.578-597
2021
Abstract
This paper proposes a novel image set classification technique based on the concept of linear regression. Unlike most other approaches, the proposed technique does not require any training. We represent the gallery image sets as subspaces in a high dimensional space. Class specific gallery subspaces are used to estimate regression models for each image in the test image set. Images of the test set are then projected onto the gallery subspaces. The residuals, calculated using the Euclidean distance between the original and the projected test images, are used as the distance metric. Three different strategies are devised to decide on the final class of the test image set. We extensively evaluated the proposed technique using both low resolution and noisy images and with less gallery data to assess the suitability of our technique for the tasks of surveillance and video-based face recognition. The experiments show that the proposed technique achieves superior classification accuracy and has a faster execution time compared with existing techniques, especially under the challenging conditions of low resolution and a limited amount of gallery and test data.
Details
- Title
- Real time surveillance for low resolution and limited data scenarios: An image set classification approach
- Authors/Creators
- U. Nadeem (Author/Creator) - The University of Western AustraliaS.A.A. Shah (Author/Creator) - Murdoch UniversityM. Bennamoun (Author/Creator) - The University of Western AustraliaR. Togneri (Author/Creator) - The University of Western AustraliaF. Sohel (Author/Creator) - Murdoch University
- Publication Details
- Information Sciences, Vol.580, pp.578-597
- Publisher
- Elsevier Inc.
- Identifiers
- 991005541574707891
- Copyright
- © 2021 Elsevier Inc.
- Murdoch Affiliation
- School of Information Technology
- Language
- English
- Resource Type
- Journal article
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InCites Highlights
These are selected metrics from InCites Benchmarking & Analytics tool, related to this output
- Collaboration types
- Domestic collaboration
- Citation topics
- 4 Electrical Engineering, Electronics & Computer Science
- 4.17 Computer Vision & Graphics
- 4.17.118 Face Recognition
- Web Of Science research areas
- Computer Science, Information Systems
- ESI research areas
- Computer Science