期刊论文

Yuan, Junsong;Xie, Wei（谢伟）

英文

Pattern Recognition

0031-3203

2017

72

285-299

10.1016/j.patcog.2017.07.028

This work is supported in part by Singapore Ministry of Education Academic Research Fund Tier 2 MOE2015-T2-2-114, and in part by grants from Office of Naval Research, US. Any opinions expressed in this material are those of the authors and do not necessarily reflect the views of ONR. It was also supported by the National Natural Science Foundation of China (61501198), Natural Science Foundation of Hubei Province (2014CFB461), and Wuhan Youth Science and Technology Chenguang program (2014072704011248).

本校为通讯机构

We present a novel spatiotemporal saliency model for object detection in videos. In contrast to previous methods focusing on exploiting or incorporating different saliency cues, the proposed method aims to use object signatures which can be identified by any kinds of object segmentation methods. We integrate two distinctive saliency maps, which are respectively computed from object proposals of an appearance-dominated method and a motion-dominated algorithm, to obtain a refined spatiotemporal saliency maps. This enables the method to achieve go...