Semiautomatic segmentation and tracking of semantic video objects

Abstract

This paper introduces a novel semantic video object extraction system using mathematical morphology and a perspective motion model. Inspired by the results from the study of the human visual system, we intend to solve the semantic video object extraction problem in two separate steps: supervised I-frame segmentation, and unsupervised P-frame tracking. First, the precise semantic video object boundary can be found using a combination of human assistance and a morphological segmentation tool. Second, the semantic video objects in the remaining frames are obtained using global perspective motion estimation and compensation of the previous semantic video object plus boundary refinement as used for I frames.