Quaternion-Based Robust PCA for Efficient Moving Target Detection and Background Recovery in Color Videos

TL;DR

This paper introduces a quaternion-based robust PCA framework with reduced computational complexity for effective moving target detection and background recovery in color videos, achieving state-of-the-art results.

Contribution

It proposes a novel quaternion Riemannian manifold approach to reduce QSVD complexity and a universal uQRPCA framework with an extension uQRPCA+ for improved background separation.

Findings

uQRPCA+ outperforms existing methods in target detection accuracy.

The quaternion Riemannian approach significantly reduces computational costs.

The method achieves state-of-the-art performance on benchmark datasets.

Abstract

Moving target detection is a challenging computer vision task aimed at generating accurate segmentation maps in diverse in-the-wild color videos captured by static cameras. If backgrounds and targets can be simultaneously extracted and recombined, such synthetic data can significantly enrich annotated in-the-wild datasets and enhance the generalization ability of deep models. Quaternion-based RPCA (QRPCA) is a promising unsupervised paradigm for color image processing. However, in color video processing, Quaternion Singular Value Decomposition (QSVD) incurs high computational costs, and rank-1 quaternion matrix fails to yield rank-1 color channels. In this paper, we reduce the computational complexity of QSVD to o(1) by utilizing a quaternion Riemannian manifold. Furthermor, we propose the universal QRPCA (uQRPCA) framework, which achieves a balance in simultaneously segmenting targets and recovering backgrounds from color videos. Moreover, we expand to uQRPCA+ by introducing the Color Rank-1 Batch (CR1B) method to further process and obtain the ideal low-rank background across color channels. Experiments demonstrate our uQRPCA+ achieves State Of The Art (SOTA) performance on moving target detection and background recovery tasks compared to existing open-source methods. Our implementation is publicly available on GitHub at https://github.com/Ruchtech/uQRPCA