Moving object detection based on incremental learning low rank representation and spatial constraint

Abstract

Background modeling and subtraction, the task to detect moving objects in a scene, is an important step in video analysis. In this paper, we present a novel moving object detection method based on Online Low Rank Matrix Recovery and graph cut from monocular video sequences. First, use the K-SVD method to initialize the dictionary to construct the background model, perform foreground detection with augmented Lagrange multipliers (ALM) and refine foreground values by spatial smooth constraint to extract the background and foreground information; Then obtain the clusters of foreground and background respectively using mean shift clustering on the background and foreground information; Third, initialize the S/T Network with corresponding image pixels as nodes (except S/T node); Calculate the data and smoothness term of graph; Finally, use max flow/minimum cut to segmentation S/T network to extract the motion objects. Online dictionary learning is adopted to update the background model. Experimental results on indoor and outdoor videos demonstrate the efficiency of our proposed method.