rSVDdpd: A Robust Scalable Video Surveillance Background Modelling Algorithm

TL;DR

This paper introduces rSVDdpd, a robust and scalable background modeling algorithm for video surveillance that effectively handles tampering, noise, and large datasets, outperforming existing PCA-based methods.

Contribution

The paper proposes a novel robust singular value decomposition technique, rSVDdpd, addressing scalability and robustness issues in background modeling for surveillance videos.

Findings

Outperforms existing robust PCA methods on benchmark datasets

Effectively handles camera tampering and noisy videos

Demonstrates scalability to large real-world datasets

Abstract

A basic algorithmic task in automated video surveillance is to separate background and foreground objects. Camera tampering, noisy videos, low frame rate, etc., pose difficulties in solving the problem. A general approach that classifies the tampered frames, and performs subsequent analysis on the remaining frames after discarding the tampered ones, results in loss of information. Several robust methods based on robust principal component analysis (PCA) have been introduced to solve this problem. To date, considerable effort has been expended to develop robust PCA via Principal Component Pursuit (PCP) methods with reduced computational cost and visually appealing foreground detection. However, the convex optimizations used in these algorithms do not scale well to real-world large datasets due to large matrix inversion steps. Also, an integral component of these foreground detection algorithms is singular value decomposition which is nonrobust. In this paper, we present a new video surveillance background modelling algorithm based on a new robust singular value decomposition technique rSVDdpd which takes care of both these issues. We also demonstrate the superiority of our proposed algorithm on a benchmark dataset and a new real-life video surveillance dataset in the presence of camera tampering. Software codes and additional illustrations are made available at the accompanying website rSVDdpd Homepage (https://subroy13.github.io/rsvddpd-home/)