LOW-RANK MATRIX DECOMPOSITION WITH SUPERPIXEL-BASED STRUCTURED SPARSE REGULARIZATION FOR MOVING OBJECT DETECTION IN SATELLITE VIDEOS
Keywords: Satellite Video, Moving Object Detection, Low-rank Matrix Decomposition, Background Subtraction, Structured Sparsity Inducing Norm
Abstract. With new accessibility to satellite videos, retrieving the dynamic information of moving objects over a vast territory becomes possible with the development of advanced video processing and machine learning techniques. Detecting moving objects can be based on the structures of both background and foreground of a satellite video, and the background is assumed to lay in a low dimensional subspace. As the moving objects in satellite videos are groups of neighbouring pixels other than isolated pixels, Low-rank and Structured Sparse Decomposition (LSD) with structured sparsity regularization on the foreground can suppress the false alarms caused by isolated outliers. However, in LSD, the groups of neighbouring pixels are extracted by a fixed sliding window over each video frame, which ignores the coherence on the appearance of a moving object. For example, a moving object can be in an irregular shape and arbitrary orientation. In this paper, we argue that the spatial groups on the foreground can be defined using the concept of superpixels, where each superpixel is formed by a group of spatially connected similar pixels obtained from over-segmentation. We conduct low-rank matrix decomposition at superpixel level, which is named as Superpixel-based LSD (S-LSD). To handle the variation in moving objects, we combine the superpixels at a range of scales in the superpixel-based spatial regularization on the foreground. With the reduction in the number of spatial groups, S-LSD presents reduced computation complexity. The results on two satellite videos show a satisfactory performance with a significant saving in processing time when the proposed S-LSD approach is applied.