期刊名称：International Journal of Advanced Robotic Systems
印刷版ISSN：1729-8806
电子版ISSN：1729-8814
出版年度：2013
卷号：10
DOI：10.5772/51832
语种：English
出版社：SAGE Publications
摘要：In this paper, we propose an online key object discovery and tracking system based on visual saliency. We formulate the problem as a temporally consistent binary labelling task on a conditional random field and solve it by using a particle filter. We also propose a context-aware saliency measurement, which can be used to improve the accuracy of any static or dynamic saliency maps. Our refined saliency maps provide clearer indications as to where the key object lies. Based on good saliency cues, we can further segment the key object inside the resulting bounding box, considering the spatial and temporal context. We tested our system extensively on different video clips. The results show that our method has significantly improved the saliency maps and tracks the key object accurately.
