Online Structured Sparsity-based Moving Object Detection from Satellite Videos
This work addresses the need for real-time moving object detection in satellite video analysis, though it is incremental as it adapts existing batch-based methods to an online setting.
The paper tackled the problem of delayed processing in batch-based moving object detection from satellite videos by proposing an online method, O-LSD, which achieved comparable accuracy and time consumption to batch approaches with significantly reduced delay.
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.