Accurate Segmentation of Moving Objects in Image Sequence Based on Spatio-Temporal Information

Abstract

Accurate segmentation of moving objects in an image sequence is a crucial task in many computer vision and image analysis applications such as the mineral processing industry and automated visual surveillance. In this paper, we introduce a novel algorithm for spatio-temporal segmentation of image sequences to achieve accurate extraction of the boundary of moving objects from noisy background. Our approach performs an initial segmentation using background subtraction method of the Gaussian mixture model (GMM). A MRF (Markov Random Field)-based labeling technique is then adopted to remove the potential miss-classified regions. The final solution is successfully obtained using the level set method, which can improve the results by splitting connected moving objects. The algorithm works well for image sequences with multiple moving objects of different sizes.