Real Time Surveillance for Low Resolution and Limited-Data Scenarios: An Image Set Classification Approach

TL;DR

This paper introduces a training-free, linear regression-based image set classification method for real-time surveillance, effective under low resolution, noise, and limited data conditions, outperforming existing techniques.

Contribution

The novel approach uses subspace representations and regression models without training or feature extraction, improving accuracy and speed in challenging scenarios.

Findings

Better classification accuracy under low resolution and limited data.

Faster execution time compared to existing methods.

Effective in surveillance, face recognition, and object recognition tasks.

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 involve any training or feature extraction. The gallery image sets are represented as subspaces in a high dimensional space. Class specific gallery subspaces are used to estimate regression models for each image of the test image set. Images of the test set are then projected on the gallery subspaces. 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 performed extensive evaluations of the proposed technique under the challenges of low resolution, noise and less gallery data for the tasks of surveillance, video-based face recognition and object recognition. Experiments show that the proposed technique achieves a better classification accuracy and a faster execution time compared to existing techniques especially under the challenging conditions of low resolution and small gallery and test data.