Fast Grayscale-Thermal Foreground Detection With Collaborative Low-Rank Decomposition

This paper investigates how to perform efficient and robust foreground detection in challenging scenarios by leveraging multiple source data. We propose a novel approach, called collaborative low-rank decomposition (CLoD), for grayscale-thermal foreground detection. Given two data matrices by accumulating sequential frames from the grayscale and the thermal videos, CLoD detects the foreground objects as sparse noises against the backgrounds with collaborative low rank structure, and also incorporates modality weights to achieve adaptive fusion of different source data. For the optimization, CLOD seeks a sub-optimal solution by making the background matrix rank explicitly determined. In particular, the background matrix with the fixed rank can be decomposed into two submatrices of low rank, and then, we iteratively optimize them and the modality weights with closed-form solutions. For improving the efficiency, we design a block-based accelerated algorithm to speed up CLOD while employing the edge-preserving algorithm to keep the accuracy. Extensive experiments on the recently public benchmark grayscale-thermal foreground detection suggest that our approach achieves comparable performance in terms of both accuracy and efficiency against other state-of-the-art methods.