Real time surveillance for low resolution and limited data scenarios: An image set classification approach

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)578-597
Number of pages20
JournalInformation Sciences
Volume580
DOIs
Publication statusPublished - Nov 2021

Fingerprint

Dive into the research topics of 'Real time surveillance for low resolution and limited data scenarios: An image set classification approach'. Together they form a unique fingerprint.

Cite this