Online Structured Sparsity-based Moving Object Detection from Satellite Videos

TL;DR

This paper introduces O-LSD, an online method for moving object detection in satellite videos that achieves real-time performance by reformulating batch low-rank decomposition into a frame-wise process.

Contribution

The paper presents a novel online low-rank and structured sparse decomposition algorithm that reduces processing delay in satellite video analysis.

Findings

O-LSD achieves comparable accuracy to batch methods.

O-LSD significantly reduces processing delay.

Theoretical convergence of O-LSD is established.

Abstract

Inspired by the recent developments in computer vision, low-rank and structured sparse matrix decomposition can be potentially be used for extract moving objects in satellite videos. This set of approaches seeks for rank minimization on the background that typically requires batch-based optimization over a sequence of frames, which causes delays in processing and limits their applications. To remedy this delay, we propose an Online Low-rank and Structured Sparse Decomposition (O-LSD). O-LSD reformulates the batch-based low-rank matrix decomposition with the structured sparse penalty to its equivalent frame-wise separable counterpart, which then defines a stochastic optimization problem for online subspace basis estimation. In order to promote online processing, O-LSD conducts the foreground and background separation and the subspace basis update alternatingly for every frame in a video. We also show the convergence of O-LSD theoretically. Experimental results on two satellite videos demonstrate the performance of O-LSD in term of accuracy and time consumption is comparable with the batch-based approaches with significantly reduced delay in processing.