Learning Via Watching: A Weakly Supervised Moving Object Detector for Satellite Videos

Abstract

Moving Object Detection (MOD) from satellite videos plays one of the most fundamental roles in satellite video surveillance. Training a supervised moving object detector typically requires boundary box annotations for object instances, however, this annotation process is time-consuming for satellite videos. In this paper, we propose a weakly supervised method for sidestepping this process, where the supervised information for detecting moving objects on a frame is instead provided by the unsupervised method based on motion knowledge across a video. We adopt the Extended Low-rank and Structured Sparse Decomposition (E-LSD) approach for generating pixel-wise pseudo labels for moving objects. Then the extracted pseudo labels are used for training a Deep Convolutional Neural Network (DCNN) following the Encoder-Decoder architecture with lateral connections for segmenting moving objects from a new frame. We demonstrate the effectiveness of the proposed method on a satellite video dataset, and, compared with five state-of-the-art MOD methods tested, it achieves both improved detection accuracy and promising frame rate.